tesseract
3.03
|
#include <classify.h>
Definition at line 60 of file classify.h.
Definition at line 35 of file classify.cpp.
: BOOL_MEMBER(prioritize_division, FALSE, "Prioritize blob division over chopping", this->params()), INT_MEMBER(tessedit_single_match, FALSE, "Top choice only from CP", this->params()), BOOL_MEMBER(classify_enable_learning, true, "Enable adaptive classifier", this->params()), INT_MEMBER(classify_debug_level, 0, "Classify debug level", this->params()), INT_MEMBER(classify_norm_method, character, "Normalization Method ...", this->params()), double_MEMBER(classify_char_norm_range, 0.2, "Character Normalization Range ...", this->params()), double_MEMBER(classify_min_norm_scale_x, 0.0, "Min char x-norm scale ...", this->params()), /* PREV DEFAULT 0.1 */ double_MEMBER(classify_max_norm_scale_x, 0.325, "Max char x-norm scale ...", this->params()), /* PREV DEFAULT 0.3 */ double_MEMBER(classify_min_norm_scale_y, 0.0, "Min char y-norm scale ...", this->params()), /* PREV DEFAULT 0.1 */ double_MEMBER(classify_max_norm_scale_y, 0.325, "Max char y-norm scale ...", this->params()), /* PREV DEFAULT 0.3 */ double_MEMBER(classify_max_rating_ratio, 1.5, "Veto ratio between classifier ratings", this->params()), double_MEMBER(classify_max_certainty_margin, 5.5, "Veto difference between classifier certainties", this->params()), BOOL_MEMBER(tess_cn_matching, 0, "Character Normalized Matching", this->params()), BOOL_MEMBER(tess_bn_matching, 0, "Baseline Normalized Matching", this->params()), BOOL_MEMBER(classify_enable_adaptive_matcher, 1, "Enable adaptive classifier", this->params()), BOOL_MEMBER(classify_use_pre_adapted_templates, 0, "Use pre-adapted classifier templates", this->params()), BOOL_MEMBER(classify_save_adapted_templates, 0, "Save adapted templates to a file", this->params()), BOOL_MEMBER(classify_enable_adaptive_debugger, 0, "Enable match debugger", this->params()), BOOL_MEMBER(classify_nonlinear_norm, 0, "Non-linear stroke-density normalization", this->params()), INT_MEMBER(matcher_debug_level, 0, "Matcher Debug Level", this->params()), INT_MEMBER(matcher_debug_flags, 0, "Matcher Debug Flags", this->params()), INT_MEMBER(classify_learning_debug_level, 0, "Learning Debug Level: ", this->params()), double_MEMBER(matcher_good_threshold, 0.125, "Good Match (0-1)", this->params()), double_MEMBER(matcher_great_threshold, 0.0, "Great Match (0-1)", this->params()), double_MEMBER(matcher_perfect_threshold, 0.02, "Perfect Match (0-1)", this->params()), double_MEMBER(matcher_bad_match_pad, 0.15, "Bad Match Pad (0-1)", this->params()), double_MEMBER(matcher_rating_margin, 0.1, "New template margin (0-1)", this->params()), double_MEMBER(matcher_avg_noise_size, 12.0, "Avg. noise blob length", this->params()), INT_MEMBER(matcher_permanent_classes_min, 1, "Min # of permanent classes", this->params()), INT_MEMBER(matcher_min_examples_for_prototyping, 3, "Reliable Config Threshold", this->params()), INT_MEMBER(matcher_sufficient_examples_for_prototyping, 5, "Enable adaption even if the ambiguities have not been seen", this->params()), double_MEMBER(matcher_clustering_max_angle_delta, 0.015, "Maximum angle delta for prototype clustering", this->params()), double_MEMBER(classify_misfit_junk_penalty, 0.0, "Penalty to apply when a non-alnum is vertically out of " "its expected textline position", this->params()), double_MEMBER(rating_scale, 1.5, "Rating scaling factor", this->params()), double_MEMBER(certainty_scale, 20.0, "Certainty scaling factor", this->params()), double_MEMBER(tessedit_class_miss_scale, 0.00390625, "Scale factor for features not used", this->params()), double_MEMBER(classify_adapted_pruning_factor, 2.5, "Prune poor adapted results this much worse than best result", this->params()), double_MEMBER(classify_adapted_pruning_threshold, -1.0, "Threshold at which classify_adapted_pruning_factor starts", this->params()), INT_MEMBER(classify_adapt_proto_threshold, 230, "Threshold for good protos during adaptive 0-255", this->params()), INT_MEMBER(classify_adapt_feature_threshold, 230, "Threshold for good features during adaptive 0-255", this->params()), BOOL_MEMBER(disable_character_fragments, TRUE, "Do not include character fragments in the" " results of the classifier", this->params()), double_MEMBER(classify_character_fragments_garbage_certainty_threshold, -3.0, "Exclude fragments that do not look like whole" " characters from training and adaption", this->params()), BOOL_MEMBER(classify_debug_character_fragments, FALSE, "Bring up graphical debugging windows for fragments training", this->params()), BOOL_MEMBER(matcher_debug_separate_windows, FALSE, "Use two different windows for debugging the matching: " "One for the protos and one for the features.", this->params()), STRING_MEMBER(classify_learn_debug_str, "", "Class str to debug learning", this->params()), INT_MEMBER(classify_class_pruner_threshold, 229, "Class Pruner Threshold 0-255", this->params()), INT_MEMBER(classify_class_pruner_multiplier, 15, "Class Pruner Multiplier 0-255: ", this->params()), INT_MEMBER(classify_cp_cutoff_strength, 7, "Class Pruner CutoffStrength: ", this->params()), INT_MEMBER(classify_integer_matcher_multiplier, 10, "Integer Matcher Multiplier 0-255: ", this->params()), EnableLearning(true), INT_MEMBER(il1_adaption_test, 0, "Dont adapt to i/I at beginning of word", this->params()), BOOL_MEMBER(classify_bln_numeric_mode, 0, "Assume the input is numbers [0-9].", this->params()), double_MEMBER(speckle_large_max_size, 0.30, "Max large speckle size", this->params()), double_MEMBER(speckle_rating_penalty, 10.0, "Penalty to add to worst rating for noise", this->params()), shape_table_(NULL), dict_(this), static_classifier_(NULL) { fontinfo_table_.set_compare_callback( NewPermanentTessCallback(CompareFontInfo)); fontinfo_table_.set_clear_callback( NewPermanentTessCallback(FontInfoDeleteCallback)); fontset_table_.set_compare_callback( NewPermanentTessCallback(CompareFontSet)); fontset_table_.set_clear_callback( NewPermanentTessCallback(FontSetDeleteCallback)); AdaptedTemplates = NULL; PreTrainedTemplates = NULL; AllProtosOn = NULL; AllConfigsOn = NULL; AllConfigsOff = NULL; TempProtoMask = NULL; NormProtos = NULL; NumAdaptationsFailed = 0; learn_debug_win_ = NULL; learn_fragmented_word_debug_win_ = NULL; learn_fragments_debug_win_ = NULL; CharNormCutoffs = new uinT16[MAX_NUM_CLASSES]; BaselineCutoffs = new uinT16[MAX_NUM_CLASSES]; }
tesseract::Classify::~Classify | ( | ) | [virtual] |
Definition at line 183 of file classify.cpp.
{ EndAdaptiveClassifier(); delete learn_debug_win_; delete learn_fragmented_word_debug_win_; delete learn_fragments_debug_win_; delete[] CharNormCutoffs; delete[] BaselineCutoffs; }
bool tesseract::Classify::AdaptableWord | ( | WERD_RES * | word | ) |
Return TRUE if the specified word is acceptable for adaptation.
Globals: none
Word | current word |
BestChoiceWord | best overall choice for word with context |
Definition at line 804 of file adaptmatch.cpp.
{ if (word->best_choice == NULL) return false; int BestChoiceLength = word->best_choice->length(); float adaptable_score = getDict().segment_penalty_dict_case_ok + ADAPTABLE_WERD_ADJUSTMENT; return // rules that apply in general - simplest to compute first BestChoiceLength > 0 && BestChoiceLength == word->rebuild_word->NumBlobs() && BestChoiceLength <= MAX_ADAPTABLE_WERD_SIZE && // This basically ensures that the word is at least a dictionary match // (freq word, user word, system dawg word, etc). // Since all the other adjustments will make adjust factor higher // than higher than adaptable_score=1.1+0.05=1.15 // Since these are other flags that ensure that the word is dict word, // this check could be at times redundant. word->best_choice->adjust_factor() <= adaptable_score && // Make sure that alternative choices are not dictionary words. word->AlternativeChoiceAdjustmentsWorseThan(adaptable_score); }
void tesseract::Classify::AdaptiveClassifier | ( | TBLOB * | Blob, |
BLOB_CHOICE_LIST * | Choices | ||
) |
This routine calls the adaptive matcher which returns (in an array) the class id of each class matched.
It also returns the number of classes matched. For each class matched it places the best rating found for that class into the Ratings array.
Bad matches are then removed so that they don't need to be sorted. The remaining good matches are then sorted and converted to choices.
This routine also performs some simple speckle filtering.
Blob | blob to be classified | |
[out] | Choices | List of choices found by adaptive matcher. filled on return with the choices found by the class pruner and the ratings therefrom. Also contains the detailed results of the integer matcher. |
Definition at line 169 of file adaptmatch.cpp.
{ assert(Choices != NULL); ADAPT_RESULTS *Results = new ADAPT_RESULTS; Results->Initialize(); ASSERT_HOST(AdaptedTemplates != NULL); DoAdaptiveMatch(Blob, Results); RemoveBadMatches(Results); Results->match.sort(CompareByRating); RemoveExtraPuncs(Results); ConvertMatchesToChoices(Blob->denorm(), Blob->bounding_box(), Results, Choices); if (matcher_debug_level >= 1) { cprintf ("AD Matches = "); PrintAdaptiveMatchResults(stdout, Results); } if (LargeSpeckle(*Blob) || Choices->length() == 0) AddLargeSpeckleTo(Results->BlobLength, Choices); #ifndef GRAPHICS_DISABLED if (classify_enable_adaptive_debugger) DebugAdaptiveClassifier(Blob, Results); #endif delete Results; } /* AdaptiveClassifier */
bool tesseract::Classify::AdaptiveClassifierIsFull | ( | ) | [inline] |
Definition at line 291 of file classify.h.
{ return NumAdaptationsFailed > 0; }
void tesseract::Classify::AdaptToChar | ( | TBLOB * | Blob, |
CLASS_ID | ClassId, | ||
int | FontinfoId, | ||
FLOAT32 | Threshold | ||
) |
Blob | blob to add to templates for ClassId |
ClassId | class to add blob to |
FontinfoId | font information from pre-trained templates |
Threshold | minimum match rating to existing template |
Globals:
Definition at line 840 of file adaptmatch.cpp.
{ int NumFeatures; INT_FEATURE_ARRAY IntFeatures; INT_RESULT_STRUCT IntResult; INT_CLASS IClass; ADAPT_CLASS Class; TEMP_CONFIG TempConfig; FEATURE_SET FloatFeatures; int NewTempConfigId; if (!LegalClassId (ClassId)) return; Class = AdaptedTemplates->Class[ClassId]; assert(Class != NULL); if (IsEmptyAdaptedClass(Class)) { InitAdaptedClass(Blob, ClassId, FontinfoId, Class, AdaptedTemplates); } else { IClass = ClassForClassId (AdaptedTemplates->Templates, ClassId); NumFeatures = GetAdaptiveFeatures(Blob, IntFeatures, &FloatFeatures); if (NumFeatures <= 0) return; // Only match configs with the matching font. BIT_VECTOR MatchingFontConfigs = NewBitVector(MAX_NUM_PROTOS); for (int cfg = 0; cfg < IClass->NumConfigs; ++cfg) { if (GetFontinfoId(Class, cfg) == FontinfoId) { SET_BIT(MatchingFontConfigs, cfg); } else { reset_bit(MatchingFontConfigs, cfg); } } im_.Match(IClass, AllProtosOn, MatchingFontConfigs, NumFeatures, IntFeatures, &IntResult, classify_adapt_feature_threshold, NO_DEBUG, matcher_debug_separate_windows); FreeBitVector(MatchingFontConfigs); SetAdaptiveThreshold(Threshold); if (IntResult.Rating <= Threshold) { if (ConfigIsPermanent (Class, IntResult.Config)) { if (classify_learning_debug_level >= 1) cprintf ("Found good match to perm config %d = %4.1f%%.\n", IntResult.Config, (1.0 - IntResult.Rating) * 100.0); FreeFeatureSet(FloatFeatures); return; } TempConfig = TempConfigFor (Class, IntResult.Config); IncreaseConfidence(TempConfig); if (TempConfig->NumTimesSeen > Class->MaxNumTimesSeen) { Class->MaxNumTimesSeen = TempConfig->NumTimesSeen; } if (classify_learning_debug_level >= 1) cprintf ("Increasing reliability of temp config %d to %d.\n", IntResult.Config, TempConfig->NumTimesSeen); if (TempConfigReliable(ClassId, TempConfig)) { MakePermanent(AdaptedTemplates, ClassId, IntResult.Config, Blob); UpdateAmbigsGroup(ClassId, Blob); } } else { if (classify_learning_debug_level >= 1) { cprintf ("Found poor match to temp config %d = %4.1f%%.\n", IntResult.Config, (1.0 - IntResult.Rating) * 100.0); if (classify_learning_debug_level > 2) DisplayAdaptedChar(Blob, IClass); } NewTempConfigId = MakeNewTemporaryConfig(AdaptedTemplates, ClassId, FontinfoId, NumFeatures, IntFeatures, FloatFeatures); if (NewTempConfigId >= 0 && TempConfigReliable(ClassId, TempConfigFor(Class, NewTempConfigId))) { MakePermanent(AdaptedTemplates, ClassId, NewTempConfigId, Blob); UpdateAmbigsGroup(ClassId, Blob); } #ifndef GRAPHICS_DISABLED if (classify_learning_debug_level > 1) { DisplayAdaptedChar(Blob, IClass); } #endif } FreeFeatureSet(FloatFeatures); } } /* AdaptToChar */
void tesseract::Classify::AddLargeSpeckleTo | ( | int | blob_length, |
BLOB_CHOICE_LIST * | choices | ||
) |
Definition at line 203 of file classify.cpp.
{ BLOB_CHOICE_IT bc_it(choices); // If there is no classifier result, we will use the worst possible certainty // and corresponding rating. float certainty = -getDict().certainty_scale; float rating = rating_scale * blob_length; if (!choices->empty() && blob_length > 0) { bc_it.move_to_last(); BLOB_CHOICE* worst_choice = bc_it.data(); // Add speckle_rating_penalty to worst rating, matching old value. rating = worst_choice->rating() + speckle_rating_penalty; // Compute the rating to correspond to the certainty. (Used to be kept // the same, but that messes up the language model search.) certainty = -rating * getDict().certainty_scale / (rating_scale * blob_length); } BLOB_CHOICE* blob_choice = new BLOB_CHOICE(UNICHAR_SPACE, rating, certainty, -1, -1, 0, 0, MAX_FLOAT32, 0, BCC_SPECKLE_CLASSIFIER); bc_it.add_to_end(blob_choice); }
void tesseract::Classify::AddNewResult | ( | ADAPT_RESULTS * | results, |
CLASS_ID | class_id, | ||
int | shape_id, | ||
FLOAT32 | rating, | ||
bool | adapted, | ||
int | config, | ||
int | fontinfo_id, | ||
int | fontinfo_id2 | ||
) |
This routine adds the result of a classification into Results. If the new rating is much worse than the current best rating, it is not entered into results because it would end up being stripped later anyway. If the new rating is better than the old rating for the class, it replaces the old rating. If this is the first rating for the class, the class is added to the list of matched classes in Results. If the new rating is better than the best so far, it becomes the best so far.
Globals:
[out] | results | results to add new result to |
class_id | class of new result | |
shape_id | shape index | |
rating | rating of new result | |
adapted | adapted match or not | |
config | config id of new result | |
fontinfo_id | font information of the new result | |
fontinfo_id2 | font information of the 2nd choice result |
Definition at line 995 of file adaptmatch.cpp.
{ ScoredClass *old_match = FindScoredUnichar(results, class_id); ScoredClass match = { class_id, shape_id, rating, adapted, static_cast<inT16>(config), static_cast<inT16>(fontinfo_id), static_cast<inT16>(fontinfo_id2) }; if (rating > results->best_match.rating + matcher_bad_match_pad || (old_match && rating >= old_match->rating)) return; if (!unicharset.get_fragment(class_id)) results->HasNonfragment = true; if (old_match) old_match->rating = rating; else results->match.push_back(match); if (rating < results->best_match.rating && // Ensure that fragments do not affect best rating, class and config. // This is needed so that at least one non-fragmented character is // always present in the results. // TODO(daria): verify that this helps accuracy and does not // hurt performance. !unicharset.get_fragment(class_id)) { results->best_match = match; } } /* AddNewResult */
void tesseract::Classify::AmbigClassifier | ( | const GenericVector< INT_FEATURE_STRUCT > & | int_features, |
const INT_FX_RESULT_STRUCT & | fx_info, | ||
const TBLOB * | blob, | ||
INT_TEMPLATES | templates, | ||
ADAPT_CLASS * | classes, | ||
UNICHAR_ID * | ambiguities, | ||
ADAPT_RESULTS * | results | ||
) |
This routine is identical to CharNormClassifier() except that it does no class pruning. It simply matches the unknown blob against the classes listed in Ambiguities.
Globals:
Blob | blob to be classified | |
Templates | built-in templates to classify against | |
Classes | adapted class templates | |
Ambiguities | array of class id's to match against | |
[out] | Results | place to put match results |
Definition at line 1057 of file adaptmatch.cpp.
{ if (int_features.empty()) return; uinT8* CharNormArray = new uinT8[unicharset.size()]; INT_RESULT_STRUCT IntResult; results->BlobLength = GetCharNormFeature(fx_info, templates, NULL, CharNormArray); bool debug = matcher_debug_level >= 2 || classify_debug_level > 1; if (debug) tprintf("AM Matches = "); int top = blob->bounding_box().top(); int bottom = blob->bounding_box().bottom(); while (*ambiguities >= 0) { CLASS_ID class_id = *ambiguities; im_.Match(ClassForClassId(templates, class_id), AllProtosOn, AllConfigsOn, int_features.size(), &int_features[0], &IntResult, classify_adapt_feature_threshold, NO_DEBUG, matcher_debug_separate_windows); ExpandShapesAndApplyCorrections(NULL, debug, class_id, bottom, top, 0, results->BlobLength, classify_integer_matcher_multiplier, CharNormArray, IntResult, results); ambiguities++; } delete [] CharNormArray; } /* AmbigClassifier */
UNICHAR_ID * tesseract::Classify::BaselineClassifier | ( | TBLOB * | Blob, |
const GenericVector< INT_FEATURE_STRUCT > & | int_features, | ||
const INT_FX_RESULT_STRUCT & | fx_info, | ||
ADAPT_TEMPLATES | Templates, | ||
ADAPT_RESULTS * | Results | ||
) |
This routine extracts baseline normalized features from the unknown character and matches them against the specified set of templates. The classes which match are added to Results.
Globals:
Blob | blob to be classified |
Templates | current set of adapted templates |
Results | place to put match results |
Definition at line 1267 of file adaptmatch.cpp.
{ if (int_features.empty()) return NULL; uinT8* CharNormArray = new uinT8[unicharset.size()]; ClearCharNormArray(CharNormArray); Results->BlobLength = IntCastRounded(fx_info.Length / kStandardFeatureLength); PruneClasses(Templates->Templates, int_features.size(), &int_features[0], CharNormArray, BaselineCutoffs, &Results->CPResults); if (matcher_debug_level >= 2 || classify_debug_level > 1) cprintf ("BL Matches = "); MasterMatcher(Templates->Templates, int_features.size(), &int_features[0], CharNormArray, Templates->Class, matcher_debug_flags, 0, Blob->bounding_box(), Results->CPResults, Results); delete [] CharNormArray; CLASS_ID ClassId = Results->best_match.unichar_id; if (ClassId == NO_CLASS) return (NULL); /* this is a bug - maybe should return "" */ return Templates->Class[ClassId]-> Config[Results->best_match.config].Perm->Ambigs; } /* BaselineClassifier */
int tesseract::Classify::CharNormClassifier | ( | TBLOB * | blob, |
const TrainingSample & | sample, | ||
ADAPT_RESULTS * | adapt_results | ||
) |
This routine extracts character normalized features from the unknown character and matches them against the specified set of templates. The classes which match are added to Results.
Blob | blob to be classified |
Templates | templates to classify unknown against |
Results | place to put match results |
Globals:
Definition at line 1317 of file adaptmatch.cpp.
{ // This is the length that is used for scaling ratings vs certainty. adapt_results->BlobLength = IntCastRounded(sample.outline_length() / kStandardFeatureLength); GenericVector<UnicharRating> unichar_results; static_classifier_->UnicharClassifySample(sample, blob->denorm().pix(), 0, -1, &unichar_results); // Convert results to the format used internally by AdaptiveClassifier. for (int r = 0; r < unichar_results.size(); ++r) { int unichar_id = unichar_results[r].unichar_id; // Fonts are listed in order of preference. int font1 = unichar_results[r].fonts.size() >= 1 ? unichar_results[r].fonts[0] : kBlankFontinfoId; int font2 = unichar_results[r].fonts.size() >= 2 ? unichar_results[r].fonts[1] : kBlankFontinfoId; float rating = 1.0f - unichar_results[r].rating; AddNewResult(adapt_results, unichar_id, -1, rating, false, 0, font1, font2); } return sample.num_features(); } /* CharNormClassifier */
int tesseract::Classify::CharNormTrainingSample | ( | bool | pruner_only, |
int | keep_this, | ||
const TrainingSample & | sample, | ||
GenericVector< UnicharRating > * | results | ||
) |
Definition at line 1342 of file adaptmatch.cpp.
{ results->clear(); ADAPT_RESULTS* adapt_results = new ADAPT_RESULTS(); adapt_results->Initialize(); // Compute the bounding box of the features. int num_features = sample.num_features(); // Only the top and bottom of the blob_box are used by MasterMatcher, so // fabricate right and left using top and bottom. TBOX blob_box(sample.geo_feature(GeoBottom), sample.geo_feature(GeoBottom), sample.geo_feature(GeoTop), sample.geo_feature(GeoTop)); // Compute the char_norm_array from the saved cn_feature. FEATURE norm_feature = sample.GetCNFeature(); uinT8* char_norm_array = new uinT8[unicharset.size()]; int num_pruner_classes = MAX(unicharset.size(), PreTrainedTemplates->NumClasses); uinT8* pruner_norm_array = new uinT8[num_pruner_classes]; adapt_results->BlobLength = static_cast<int>(ActualOutlineLength(norm_feature) * 20 + 0.5); ComputeCharNormArrays(norm_feature, PreTrainedTemplates, char_norm_array, pruner_norm_array); PruneClasses(PreTrainedTemplates, num_features, sample.features(), pruner_norm_array, shape_table_ != NULL ? &shapetable_cutoffs_[0] : CharNormCutoffs, &adapt_results->CPResults); delete [] pruner_norm_array; if (keep_this >= 0) { adapt_results->CPResults[0].Class = keep_this; adapt_results->CPResults.truncate(1); } if (pruner_only) { // Convert pruner results to output format. for (int i = 0; i < adapt_results->CPResults.size(); ++i) { int class_id = adapt_results->CPResults[i].Class; results->push_back( UnicharRating(class_id, 1.0f - adapt_results->CPResults[i].Rating)); } } else { MasterMatcher(PreTrainedTemplates, num_features, sample.features(), char_norm_array, NULL, matcher_debug_flags, classify_integer_matcher_multiplier, blob_box, adapt_results->CPResults, adapt_results); // Convert master matcher results to output format. for (int i = 0; i < adapt_results->match.size(); i++) { ScoredClass next = adapt_results->match[i]; UnicharRating rating(next.unichar_id, 1.0f - next.rating); if (next.fontinfo_id >= 0) { rating.fonts.push_back(next.fontinfo_id); if (next.fontinfo_id2 >= 0) rating.fonts.push_back(next.fontinfo_id2); } results->push_back(rating); } results->sort(&UnicharRating::SortDescendingRating); } delete [] char_norm_array; delete adapt_results; return num_features; } /* CharNormTrainingSample */
int tesseract::Classify::ClassAndConfigIDToFontOrShapeID | ( | int | class_id, |
int | int_result_config | ||
) | const |
Definition at line 2299 of file adaptmatch.cpp.
{ int font_set_id = PreTrainedTemplates->Class[class_id]->font_set_id; // Older inttemps have no font_ids. if (font_set_id < 0) return kBlankFontinfoId; const FontSet &fs = fontset_table_.get(font_set_id); ASSERT_HOST(int_result_config >= 0 && int_result_config < fs.size); return fs.configs[int_result_config]; }
STRING tesseract::Classify::ClassIDToDebugStr | ( | const INT_TEMPLATES_STRUCT * | templates, |
int | class_id, | ||
int | config_id | ||
) | const |
Definition at line 2286 of file adaptmatch.cpp.
{ STRING class_string; if (templates == PreTrainedTemplates && shape_table_ != NULL) { int shape_id = ClassAndConfigIDToFontOrShapeID(class_id, config_id); class_string = shape_table_->DebugStr(shape_id); } else { class_string = unicharset.debug_str(class_id); } return class_string; }
void tesseract::Classify::ClassifyAsNoise | ( | ADAPT_RESULTS * | Results | ) |
This routine computes a rating which reflects the likelihood that the blob being classified is a noise blob. NOTE: assumes that the blob length has already been computed and placed into Results.
Results | results to add noise classification to |
Globals:
Definition at line 1422 of file adaptmatch.cpp.
{ register FLOAT32 Rating; Rating = Results->BlobLength / matcher_avg_noise_size; Rating *= Rating; Rating /= 1.0 + Rating; AddNewResult(Results, NO_CLASS, -1, Rating, false, -1, kBlankFontinfoId, kBlankFontinfoId); } /* ClassifyAsNoise */
void tesseract::Classify::ClearCharNormArray | ( | uinT8 * | char_norm_array | ) |
For each class in the unicharset, clears the corresponding entry in char_norm_array. char_norm_array is indexed by unichar_id.
Globals:
char_norm_array | array to be cleared |
Definition at line 48 of file float2int.cpp.
{ memset(char_norm_array, 0, sizeof(*char_norm_array) * unicharset.size()); } /* ClearCharNormArray */
void tesseract::Classify::ComputeCharNormArrays | ( | FEATURE_STRUCT * | norm_feature, |
INT_TEMPLATES_STRUCT * | templates, | ||
uinT8 * | char_norm_array, | ||
uinT8 * | pruner_array | ||
) |
Definition at line 1774 of file adaptmatch.cpp.
{ ComputeIntCharNormArray(*norm_feature, char_norm_array); if (pruner_array != NULL) { if (shape_table_ == NULL) { ComputeIntCharNormArray(*norm_feature, pruner_array); } else { memset(pruner_array, MAX_UINT8, templates->NumClasses * sizeof(pruner_array[0])); // Each entry in the pruner norm array is the MIN of all the entries of // the corresponding unichars in the CharNormArray. for (int id = 0; id < templates->NumClasses; ++id) { int font_set_id = templates->Class[id]->font_set_id; const FontSet &fs = fontset_table_.get(font_set_id); for (int config = 0; config < fs.size; ++config) { const Shape& shape = shape_table_->GetShape(fs.configs[config]); for (int c = 0; c < shape.size(); ++c) { if (char_norm_array[shape[c].unichar_id] < pruner_array[id]) pruner_array[id] = char_norm_array[shape[c].unichar_id]; } } } } } FreeFeature(norm_feature); }
double tesseract::Classify::ComputeCorrectedRating | ( | bool | debug, |
int | unichar_id, | ||
double | cp_rating, | ||
double | im_rating, | ||
int | feature_misses, | ||
int | bottom, | ||
int | top, | ||
int | blob_length, | ||
int | matcher_multiplier, | ||
const uinT8 * | cn_factors | ||
) |
Definition at line 1204 of file adaptmatch.cpp.
{ // Compute class feature corrections. double cn_corrected = im_.ApplyCNCorrection(im_rating, blob_length, cn_factors[unichar_id], matcher_multiplier); double miss_penalty = tessedit_class_miss_scale * feature_misses; double vertical_penalty = 0.0; // Penalize non-alnums for being vertical misfits. if (!unicharset.get_isalpha(unichar_id) && !unicharset.get_isdigit(unichar_id) && cn_factors[unichar_id] != 0 && classify_misfit_junk_penalty > 0.0) { int min_bottom, max_bottom, min_top, max_top; unicharset.get_top_bottom(unichar_id, &min_bottom, &max_bottom, &min_top, &max_top); if (debug) { tprintf("top=%d, vs [%d, %d], bottom=%d, vs [%d, %d]\n", top, min_top, max_top, bottom, min_bottom, max_bottom); } if (top < min_top || top > max_top || bottom < min_bottom || bottom > max_bottom) { vertical_penalty = classify_misfit_junk_penalty; } } double result =cn_corrected + miss_penalty + vertical_penalty; if (result > WORST_POSSIBLE_RATING) result = WORST_POSSIBLE_RATING; if (debug) { tprintf("%s: %2.1f(CP%2.1f, IM%2.1f + CN%.2f(%d) + MP%2.1f + VP%2.1f)\n", unicharset.id_to_unichar(unichar_id), result * 100.0, cp_rating * 100.0, im_rating * 100.0, (cn_corrected - im_rating) * 100.0, cn_factors[unichar_id], miss_penalty * 100.0, vertical_penalty * 100.0); } return result; }
void tesseract::Classify::ComputeIntCharNormArray | ( | const FEATURE_STRUCT & | norm_feature, |
uinT8 * | char_norm_array | ||
) |
For each class in unicharset, computes the match between norm_feature and the normalization protos for that class. Converts this number to the range from 0 - 255 and stores it into char_norm_array. CharNormArray is indexed by unichar_id.
Globals:
norm_feature | character normalization feature | |
[out] | char_norm_array | place to put results of size unicharset.size() |
Definition at line 69 of file float2int.cpp.
{ for (int i = 0; i < unicharset.size(); i++) { if (i < PreTrainedTemplates->NumClasses) { int norm_adjust = static_cast<int>(INT_CHAR_NORM_RANGE * ComputeNormMatch(i, norm_feature, FALSE)); char_norm_array[i] = ClipToRange(norm_adjust, 0, MAX_INT_CHAR_NORM); } else { // Classes with no templates (eg. ambigs & ligatures) default // to worst match. char_norm_array[i] = MAX_INT_CHAR_NORM; } } } /* ComputeIntCharNormArray */
void tesseract::Classify::ComputeIntFeatures | ( | FEATURE_SET | Features, |
INT_FEATURE_ARRAY | IntFeatures | ||
) |
This routine converts each floating point pico-feature in Features into integer format and saves it into IntFeatures.
Globals:
Features | floating point pico-features to be converted | |
[out] | IntFeatures | array to put converted features into |
Definition at line 100 of file float2int.cpp.
{ int Fid; FEATURE Feature; FLOAT32 YShift; if (classify_norm_method == baseline) YShift = BASELINE_Y_SHIFT; else YShift = Y_SHIFT; for (Fid = 0; Fid < Features->NumFeatures; Fid++) { Feature = Features->Features[Fid]; IntFeatures[Fid].X = BucketFor (Feature->Params[PicoFeatX], X_SHIFT, INT_FEAT_RANGE); IntFeatures[Fid].Y = BucketFor (Feature->Params[PicoFeatY], YShift, INT_FEAT_RANGE); IntFeatures[Fid].Theta = CircBucketFor (Feature->Params[PicoFeatDir], ANGLE_SHIFT, INT_FEAT_RANGE); IntFeatures[Fid].CP_misses = 0; } } /* ComputeIntFeatures */
FLOAT32 tesseract::Classify::ComputeNormMatch | ( | CLASS_ID | ClassId, |
const FEATURE_STRUCT & | feature, | ||
BOOL8 | DebugMatch | ||
) |
Definition at line 73 of file normmatch.cpp.
{ /* ** Parameters: ** ClassId id of class to match against ** Feature character normalization feature ** DebugMatch controls dump of debug info ** Globals: ** NormProtos character normalization prototypes ** Operation: This routine compares Features against each character ** normalization proto for ClassId and returns the match ** rating of the best match. ** Return: Best match rating for Feature against protos of ClassId. ** Exceptions: none ** History: Wed Dec 19 16:56:12 1990, DSJ, Created. */ LIST Protos; FLOAT32 BestMatch; FLOAT32 Match; FLOAT32 Delta; PROTOTYPE *Proto; int ProtoId; if (ClassId >= NormProtos->NumProtos) { ClassId = NO_CLASS; } /* handle requests for classification as noise */ if (ClassId == NO_CLASS) { /* kludge - clean up constants and make into control knobs later */ Match = (feature.Params[CharNormLength] * feature.Params[CharNormLength] * 500.0 + feature.Params[CharNormRx] * feature.Params[CharNormRx] * 8000.0 + feature.Params[CharNormRy] * feature.Params[CharNormRy] * 8000.0); return (1.0 - NormEvidenceOf (Match)); } BestMatch = MAX_FLOAT32; Protos = NormProtos->Protos[ClassId]; if (DebugMatch) { tprintf("\nChar norm for class %s\n", unicharset.id_to_unichar(ClassId)); } ProtoId = 0; iterate(Protos) { Proto = (PROTOTYPE *) first_node (Protos); Delta = feature.Params[CharNormY] - Proto->Mean[CharNormY]; Match = Delta * Delta * Proto->Weight.Elliptical[CharNormY]; if (DebugMatch) { tprintf("YMiddle: Proto=%g, Delta=%g, Var=%g, Dist=%g\n", Proto->Mean[CharNormY], Delta, Proto->Weight.Elliptical[CharNormY], Match); } Delta = feature.Params[CharNormRx] - Proto->Mean[CharNormRx]; Match += Delta * Delta * Proto->Weight.Elliptical[CharNormRx]; if (DebugMatch) { tprintf("Height: Proto=%g, Delta=%g, Var=%g, Dist=%g\n", Proto->Mean[CharNormRx], Delta, Proto->Weight.Elliptical[CharNormRx], Match); } // Ry is width! See intfx.cpp. Delta = feature.Params[CharNormRy] - Proto->Mean[CharNormRy]; if (DebugMatch) { tprintf("Width: Proto=%g, Delta=%g, Var=%g\n", Proto->Mean[CharNormRy], Delta, Proto->Weight.Elliptical[CharNormRy]); } Delta = Delta * Delta * Proto->Weight.Elliptical[CharNormRy]; Delta *= kWidthErrorWeighting; Match += Delta; if (DebugMatch) { tprintf("Total Dist=%g, scaled=%g, sigmoid=%g, penalty=%g\n", Match, Match / classify_norm_adj_midpoint, NormEvidenceOf(Match), 256 * (1 - NormEvidenceOf(Match))); } if (Match < BestMatch) BestMatch = Match; ProtoId++; } return 1.0 - NormEvidenceOf(BestMatch); } /* ComputeNormMatch */
void tesseract::Classify::ConvertMatchesToChoices | ( | const DENORM & | denorm, |
const TBOX & | box, | ||
ADAPT_RESULTS * | Results, | ||
BLOB_CHOICE_LIST * | Choices | ||
) |
The function converts the given match ratings to the list of blob choices with ratings and certainties (used by the context checkers). If character fragments are present in the results, this function also makes sure that there is at least one non-fragmented classification included. For each classification result check the unicharset for "definite" ambiguities and modify the resulting Choices accordingly.
Definition at line 1482 of file adaptmatch.cpp.
{ assert(Choices != NULL); FLOAT32 Rating; FLOAT32 Certainty; BLOB_CHOICE_IT temp_it; bool contains_nonfrag = false; temp_it.set_to_list(Choices); int choices_length = 0; // With no shape_table_ maintain the previous MAX_MATCHES as the maximum // number of returned results, but with a shape_table_ we want to have room // for at least the biggest shape (which might contain hundreds of Indic // grapheme fragments) and more, so use double the size of the biggest shape // if that is more than the default. int max_matches = MAX_MATCHES; if (shape_table_ != NULL) { max_matches = shape_table_->MaxNumUnichars() * 2; if (max_matches < MAX_MATCHES) max_matches = MAX_MATCHES; } float best_certainty = -MAX_FLOAT32; for (int i = 0; i < Results->match.size(); i++) { ScoredClass next = Results->match[i]; int fontinfo_id = next.fontinfo_id; int fontinfo_id2 = next.fontinfo_id2; bool adapted = next.adapted; bool current_is_frag = (unicharset.get_fragment(next.unichar_id) != NULL); if (temp_it.length()+1 == max_matches && !contains_nonfrag && current_is_frag) { continue; // look for a non-fragmented character to fill the // last spot in Choices if only fragments are present } // BlobLength can never be legally 0, this means recognition failed. // But we must return a classification result because some invoking // functions (chopper/permuter) do not anticipate a null blob choice. // So we need to assign a poor, but not infinitely bad score. if (Results->BlobLength == 0) { Certainty = -20; Rating = 100; // should be -certainty * real_blob_length } else { Rating = Certainty = next.rating; Rating *= rating_scale * Results->BlobLength; Certainty *= -(getDict().certainty_scale); } // Adapted results, by their very nature, should have good certainty. // Those that don't are at best misleading, and often lead to errors, // so don't accept adapted results that are too far behind the best result, // whether adapted or static. // TODO(rays) find some way of automatically tuning these constants. if (Certainty > best_certainty) { best_certainty = MIN(Certainty, classify_adapted_pruning_threshold); } else if (adapted && Certainty / classify_adapted_pruning_factor < best_certainty) { continue; // Don't accept bad adapted results. } float min_xheight, max_xheight, yshift; denorm.XHeightRange(next.unichar_id, unicharset, box, &min_xheight, &max_xheight, &yshift); temp_it.add_to_end(new BLOB_CHOICE(next.unichar_id, Rating, Certainty, fontinfo_id, fontinfo_id2, unicharset.get_script(next.unichar_id), min_xheight, max_xheight, yshift, adapted ? BCC_ADAPTED_CLASSIFIER : BCC_STATIC_CLASSIFIER)); contains_nonfrag |= !current_is_frag; // update contains_nonfrag choices_length++; if (choices_length >= max_matches) break; } Results->match.truncate(choices_length); } // ConvertMatchesToChoices
void tesseract::Classify::ConvertProto | ( | PROTO | Proto, |
int | ProtoId, | ||
INT_CLASS | Class | ||
) |
Definition at line 545 of file intproto.cpp.
{ /* ** Parameters: ** Proto floating-pt proto to be converted to integer format ** ProtoId id of proto ** Class integer class to add converted proto to ** Globals: none ** Operation: This routine converts Proto to integer format and ** installs it as ProtoId in Class. ** Return: none ** Exceptions: none ** History: Fri Feb 8 11:22:43 1991, DSJ, Created. */ INT_PROTO P; FLOAT32 Param; assert(ProtoId < Class->NumProtos); P = ProtoForProtoId(Class, ProtoId); Param = Proto->A * 128; P->A = TruncateParam(Param, -128, 127, NULL); Param = -Proto->B * 256; P->B = TruncateParam(Param, 0, 255, NULL); Param = Proto->C * 128; P->C = TruncateParam(Param, -128, 127, NULL); Param = Proto->Angle * 256; if (Param < 0 || Param >= 256) P->Angle = 0; else P->Angle = (uinT8) Param; /* round proto length to nearest integer number of pico-features */ Param = (Proto->Length / GetPicoFeatureLength()) + 0.5; Class->ProtoLengths[ProtoId] = TruncateParam(Param, 1, 255, NULL); if (classify_learning_debug_level >= 2) cprintf("Converted ffeat to (A=%d,B=%d,C=%d,L=%d)", P->A, P->B, P->C, Class->ProtoLengths[ProtoId]); } /* ConvertProto */
INT_TEMPLATES tesseract::Classify::CreateIntTemplates | ( | CLASSES | FloatProtos, |
const UNICHARSET & | target_unicharset | ||
) |
Definition at line 590 of file intproto.cpp.
{ /* ** Parameters: ** FloatProtos prototypes in old floating pt format ** Globals: none ** Operation: This routine converts from the old floating point format ** to the new integer format. ** Return: New set of training templates in integer format. ** Exceptions: none ** History: Thu Feb 7 14:40:42 1991, DSJ, Created. */ INT_TEMPLATES IntTemplates; CLASS_TYPE FClass; INT_CLASS IClass; int ClassId; int ProtoId; int ConfigId; IntTemplates = NewIntTemplates(); for (ClassId = 0; ClassId < target_unicharset.size(); ClassId++) { FClass = &(FloatProtos[ClassId]); if (FClass->NumProtos == 0 && FClass->NumConfigs == 0 && strcmp(target_unicharset.id_to_unichar(ClassId), " ") != 0) { cprintf("Warning: no protos/configs for %s in CreateIntTemplates()\n", target_unicharset.id_to_unichar(ClassId)); } assert(UnusedClassIdIn(IntTemplates, ClassId)); IClass = NewIntClass(FClass->NumProtos, FClass->NumConfigs); FontSet fs; fs.size = FClass->font_set.size(); fs.configs = new int[fs.size]; for (int i = 0; i < fs.size; ++i) { fs.configs[i] = FClass->font_set.get(i); } if (this->fontset_table_.contains(fs)) { IClass->font_set_id = this->fontset_table_.get_id(fs); delete[] fs.configs; } else { IClass->font_set_id = this->fontset_table_.push_back(fs); } AddIntClass(IntTemplates, ClassId, IClass); for (ProtoId = 0; ProtoId < FClass->NumProtos; ProtoId++) { AddIntProto(IClass); ConvertProto(ProtoIn(FClass, ProtoId), ProtoId, IClass); AddProtoToProtoPruner(ProtoIn(FClass, ProtoId), ProtoId, IClass, classify_learning_debug_level >= 2); AddProtoToClassPruner(ProtoIn(FClass, ProtoId), ClassId, IntTemplates); } for (ConfigId = 0; ConfigId < FClass->NumConfigs; ConfigId++) { AddIntConfig(IClass); ConvertConfig(FClass->Configurations[ConfigId], ConfigId, IClass); } } return (IntTemplates); } /* CreateIntTemplates */
void tesseract::Classify::DebugAdaptiveClassifier | ( | TBLOB * | blob, |
ADAPT_RESULTS * | Results | ||
) |
Blob | blob whose classification is being debugged |
Results | results of match being debugged |
Globals: none
Definition at line 1569 of file adaptmatch.cpp.
{ if (static_classifier_ == NULL) return; for (int i = 0; i < Results->match.size(); i++) { if (i == 0 || Results->match[i].rating < Results->best_match.rating) Results->best_match = Results->match[i]; } INT_FX_RESULT_STRUCT fx_info; GenericVector<INT_FEATURE_STRUCT> bl_features; TrainingSample* sample = BlobToTrainingSample(*blob, false, &fx_info, &bl_features); if (sample == NULL) return; static_classifier_->DebugDisplay(*sample, blob->denorm().pix(), Results->best_match.unichar_id); } /* DebugAdaptiveClassifier */
void tesseract::Classify::DisplayAdaptedChar | ( | TBLOB * | blob, |
INT_CLASS_STRUCT * | int_class | ||
) |
Definition at line 937 of file adaptmatch.cpp.
{ #ifndef GRAPHICS_DISABLED INT_FX_RESULT_STRUCT fx_info; GenericVector<INT_FEATURE_STRUCT> bl_features; TrainingSample* sample = BlobToTrainingSample(*blob, classify_nonlinear_norm, &fx_info, &bl_features); if (sample == NULL) return; INT_RESULT_STRUCT IntResult; im_.Match(int_class, AllProtosOn, AllConfigsOn, bl_features.size(), &bl_features[0], &IntResult, classify_adapt_feature_threshold, NO_DEBUG, matcher_debug_separate_windows); cprintf ("Best match to temp config %d = %4.1f%%.\n", IntResult.Config, (1.0 - IntResult.Rating) * 100.0); if (classify_learning_debug_level >= 2) { uinT32 ConfigMask; ConfigMask = 1 << IntResult.Config; ShowMatchDisplay(); im_.Match(int_class, AllProtosOn, (BIT_VECTOR)&ConfigMask, bl_features.size(), &bl_features[0], &IntResult, classify_adapt_feature_threshold, 6 | 0x19, matcher_debug_separate_windows); UpdateMatchDisplay(); } #endif }
void tesseract::Classify::DoAdaptiveMatch | ( | TBLOB * | Blob, |
ADAPT_RESULTS * | Results | ||
) |
This routine performs an adaptive classification. If we have not yet adapted to enough classes, a simple classification to the pre-trained templates is performed. Otherwise, we match the blob against the adapted templates. If the adapted templates do not match well, we try a match against the pre-trained templates. If an adapted template match is found, we do a match to any pre-trained templates which could be ambiguous. The results from all of these classifications are merged together into Results.
Blob | blob to be classified |
Results | place to put match results |
Globals:
Definition at line 1609 of file adaptmatch.cpp.
{ UNICHAR_ID *Ambiguities; INT_FX_RESULT_STRUCT fx_info; GenericVector<INT_FEATURE_STRUCT> bl_features; TrainingSample* sample = BlobToTrainingSample(*Blob, classify_nonlinear_norm, &fx_info, &bl_features); if (sample == NULL) return; if (AdaptedTemplates->NumPermClasses < matcher_permanent_classes_min || tess_cn_matching) { CharNormClassifier(Blob, *sample, Results); } else { Ambiguities = BaselineClassifier(Blob, bl_features, fx_info, AdaptedTemplates, Results); if ((!Results->match.empty() && MarginalMatch(Results->best_match.rating) && !tess_bn_matching) || Results->match.empty()) { CharNormClassifier(Blob, *sample, Results); } else if (Ambiguities && *Ambiguities >= 0 && !tess_bn_matching) { AmbigClassifier(bl_features, fx_info, Blob, PreTrainedTemplates, AdaptedTemplates->Class, Ambiguities, Results); } } // Force the blob to be classified as noise // if the results contain only fragments. // TODO(daria): verify that this is better than // just adding a NULL classification. if (!Results->HasNonfragment || Results->match.empty()) ClassifyAsNoise(Results); delete sample; } /* DoAdaptiveMatch */
This routine performs cleanup operations on the adaptive classifier. It should be called before the program is terminated. Its main function is to save the adapted templates to a file.
Globals:
Definition at line 434 of file adaptmatch.cpp.
{ STRING Filename; FILE *File; #ifndef SECURE_NAMES if (AdaptedTemplates != NULL && classify_enable_adaptive_matcher && classify_save_adapted_templates) { Filename = imagefile + ADAPT_TEMPLATE_SUFFIX; File = fopen (Filename.string(), "wb"); if (File == NULL) cprintf ("Unable to save adapted templates to %s!\n", Filename.string()); else { cprintf ("\nSaving adapted templates to %s ...", Filename.string()); fflush(stdout); WriteAdaptedTemplates(File, AdaptedTemplates); cprintf ("\n"); fclose(File); } } #endif if (AdaptedTemplates != NULL) { free_adapted_templates(AdaptedTemplates); AdaptedTemplates = NULL; } if (PreTrainedTemplates != NULL) { free_int_templates(PreTrainedTemplates); PreTrainedTemplates = NULL; } getDict().EndDangerousAmbigs(); FreeNormProtos(); if (AllProtosOn != NULL) { FreeBitVector(AllProtosOn); FreeBitVector(AllConfigsOn); FreeBitVector(AllConfigsOff); FreeBitVector(TempProtoMask); AllProtosOn = NULL; AllConfigsOn = NULL; AllConfigsOff = NULL; TempProtoMask = NULL; } delete shape_table_; shape_table_ = NULL; if (static_classifier_ != NULL) { delete static_classifier_; static_classifier_ = NULL; } } /* EndAdaptiveClassifier */
void tesseract::Classify::ExpandShapesAndApplyCorrections | ( | ADAPT_CLASS * | classes, |
bool | debug, | ||
int | class_id, | ||
int | bottom, | ||
int | top, | ||
float | cp_rating, | ||
int | blob_length, | ||
int | matcher_multiplier, | ||
const uinT8 * | cn_factors, | ||
INT_RESULT_STRUCT & | int_result, | ||
ADAPT_RESULTS * | final_results | ||
) |
Definition at line 1138 of file adaptmatch.cpp.
{ // Compute the fontinfo_ids. int fontinfo_id = kBlankFontinfoId; int fontinfo_id2 = kBlankFontinfoId; if (classes != NULL) { // Adapted result. fontinfo_id = GetFontinfoId(classes[class_id], int_result.Config); if (int_result.Config2 >= 0) fontinfo_id2 = GetFontinfoId(classes[class_id], int_result.Config2); } else { // Pre-trained result. fontinfo_id = ClassAndConfigIDToFontOrShapeID(class_id, int_result.Config); if (int_result.Config2 >= 0) { fontinfo_id2 = ClassAndConfigIDToFontOrShapeID(class_id, int_result.Config2); } if (shape_table_ != NULL) { // Actually fontinfo_id is an index into the shape_table_ and it // contains a list of unchar_id/font_id pairs. int shape_id = fontinfo_id; const Shape& shape = shape_table_->GetShape(fontinfo_id); double min_rating = 0.0; for (int c = 0; c < shape.size(); ++c) { int unichar_id = shape[c].unichar_id; fontinfo_id = shape[c].font_ids[0]; if (shape[c].font_ids.size() > 1) fontinfo_id2 = shape[c].font_ids[1]; else if (fontinfo_id2 != kBlankFontinfoId) fontinfo_id2 = shape_table_->GetShape(fontinfo_id2)[0].font_ids[0]; double rating = ComputeCorrectedRating(debug, unichar_id, cp_rating, int_result.Rating, int_result.FeatureMisses, bottom, top, blob_length, matcher_multiplier, cn_factors); if (c == 0 || rating < min_rating) min_rating = rating; if (unicharset.get_enabled(unichar_id)) { AddNewResult(final_results, unichar_id, shape_id, rating, classes != NULL, int_result.Config, fontinfo_id, fontinfo_id2); } } int_result.Rating = min_rating; return; } } double rating = ComputeCorrectedRating(debug, class_id, cp_rating, int_result.Rating, int_result.FeatureMisses, bottom, top, blob_length, matcher_multiplier, cn_factors); if (unicharset.get_enabled(class_id)) { AddNewResult(final_results, class_id, -1, rating, classes != NULL, int_result.Config, fontinfo_id, fontinfo_id2); } int_result.Rating = rating; }
void tesseract::Classify::ExtractFeatures | ( | const TBLOB & | blob, |
bool | nonlinear_norm, | ||
GenericVector< INT_FEATURE_STRUCT > * | bl_features, | ||
GenericVector< INT_FEATURE_STRUCT > * | cn_features, | ||
INT_FX_RESULT_STRUCT * | results, | ||
GenericVector< int > * | outline_cn_counts | ||
) | [static] |
Definition at line 445 of file intfx.cpp.
{ DENORM bl_denorm, cn_denorm; tesseract::Classify::SetupBLCNDenorms(blob, nonlinear_norm, &bl_denorm, &cn_denorm, results); if (outline_cn_counts != NULL) outline_cn_counts->truncate(0); // Iterate the outlines. for (TESSLINE* ol = blob.outlines; ol != NULL; ol = ol->next) { // Iterate the polygon. EDGEPT* loop_pt = ol->FindBestStartPt(); EDGEPT* pt = loop_pt; if (pt == NULL) continue; do { if (pt->IsHidden()) continue; // Find a run of equal src_outline. EDGEPT* last_pt = pt; do { last_pt = last_pt->next; } while (last_pt != loop_pt && !last_pt->IsHidden() && last_pt->src_outline == pt->src_outline); last_pt = last_pt->prev; // Until the adaptive classifier can be weaned off polygon segments, // we have to force extraction from the polygon for the bl_features. ExtractFeaturesFromRun(pt, last_pt, bl_denorm, kStandardFeatureLength, true, bl_features); ExtractFeaturesFromRun(pt, last_pt, cn_denorm, kStandardFeatureLength, false, cn_features); pt = last_pt; } while ((pt = pt->next) != loop_pt); if (outline_cn_counts != NULL) outline_cn_counts->push_back(cn_features->size()); } results->NumBL = bl_features->size(); results->NumCN = cn_features->size(); results->YBottom = blob.bounding_box().bottom(); results->YTop = blob.bounding_box().top(); results->Width = blob.bounding_box().width(); }
Definition at line 36 of file outfeat.cpp.
{ /* ** Parameters: ** Blob blob to extract pico-features from ** LineStats statistics on text row blob is in ** Globals: none ** Operation: Convert each segment in the outline to a feature ** and return the features. ** Return: Outline-features for Blob. ** Exceptions: none ** History: 11/13/90, DSJ, Created. ** 05/24/91, DSJ, Updated for either char or baseline normalize. */ LIST Outlines; LIST RemainingOutlines; MFOUTLINE Outline; FEATURE_SET FeatureSet; FLOAT32 XScale, YScale; FeatureSet = NewFeatureSet (MAX_OUTLINE_FEATURES); if (Blob == NULL) return (FeatureSet); Outlines = ConvertBlob (Blob); NormalizeOutlines(Outlines, &XScale, &YScale); RemainingOutlines = Outlines; iterate(RemainingOutlines) { Outline = (MFOUTLINE) first_node (RemainingOutlines); ConvertToOutlineFeatures(Outline, FeatureSet); } if (classify_norm_method == baseline) NormalizeOutlineX(FeatureSet); FreeOutlines(Outlines); return (FeatureSet); } /* ExtractOutlineFeatures */
Definition at line 57 of file picofeat.cpp.
{ /* ** Parameters: ** Blob blob to extract pico-features from ** LineStats statistics on text row blob is in ** Globals: ** classify_norm_method normalization method currently specified ** Operation: Dummy for now. ** Return: Pico-features for Blob. ** Exceptions: none ** History: 9/4/90, DSJ, Created. */ LIST Outlines; LIST RemainingOutlines; MFOUTLINE Outline; FEATURE_SET FeatureSet; FLOAT32 XScale, YScale; FeatureSet = NewFeatureSet(MAX_PICO_FEATURES); Outlines = ConvertBlob(Blob); NormalizeOutlines(Outlines, &XScale, &YScale); RemainingOutlines = Outlines; iterate(RemainingOutlines) { Outline = (MFOUTLINE) first_node (RemainingOutlines); ConvertToPicoFeatures2(Outline, FeatureSet); } if (classify_norm_method == baseline) NormalizePicoX(FeatureSet); FreeOutlines(Outlines); return (FeatureSet); } /* ExtractPicoFeatures */
void tesseract::Classify::FreeNormProtos | ( | ) |
Definition at line 161 of file normmatch.cpp.
{ if (NormProtos != NULL) { for (int i = 0; i < NormProtos->NumProtos; i++) FreeProtoList(&NormProtos->Protos[i]); Efree(NormProtos->Protos); Efree(NormProtos->ParamDesc); Efree(NormProtos); NormProtos = NULL; } }
UnicityTable<FontInfo>& tesseract::Classify::get_fontinfo_table | ( | ) | [inline] |
Definition at line 349 of file classify.h.
{ return fontinfo_table_; }
const UnicityTable<FontInfo>& tesseract::Classify::get_fontinfo_table | ( | ) | const [inline] |
Definition at line 352 of file classify.h.
{ return fontinfo_table_; }
UnicityTable<FontSet>& tesseract::Classify::get_fontset_table | ( | ) | [inline] |
Definition at line 355 of file classify.h.
{ return fontset_table_; }
int tesseract::Classify::GetAdaptiveFeatures | ( | TBLOB * | Blob, |
INT_FEATURE_ARRAY | IntFeatures, | ||
FEATURE_SET * | FloatFeatures | ||
) |
This routine sets up the feature extractor to extract baseline normalized pico-features.
The extracted pico-features are converted to integer form and placed in IntFeatures. The original floating-pt. features are returned in FloatFeatures.
Globals: none
Blob | blob to extract features from | |
[out] | IntFeatures | array to fill with integer features |
[out] | FloatFeatures | place to return actual floating-pt features |
Definition at line 765 of file adaptmatch.cpp.
{ FEATURE_SET Features; int NumFeatures; classify_norm_method.set_value(baseline); Features = ExtractPicoFeatures(Blob); NumFeatures = Features->NumFeatures; if (NumFeatures > UNLIKELY_NUM_FEAT) { FreeFeatureSet(Features); return 0; } ComputeIntFeatures(Features, IntFeatures); *FloatFeatures = Features; return NumFeatures; } /* GetAdaptiveFeatures */
UNICHAR_ID * tesseract::Classify::GetAmbiguities | ( | TBLOB * | Blob, |
CLASS_ID | CorrectClass | ||
) |
This routine matches blob to the built-in templates to find out if there are any classes other than the correct class which are potential ambiguities.
Blob | blob to get classification ambiguities for |
CorrectClass | correct class for Blob |
Globals:
Definition at line 1664 of file adaptmatch.cpp.
{ ADAPT_RESULTS *Results = new ADAPT_RESULTS(); UNICHAR_ID *Ambiguities; int i; Results->Initialize(); INT_FX_RESULT_STRUCT fx_info; GenericVector<INT_FEATURE_STRUCT> bl_features; TrainingSample* sample = BlobToTrainingSample(*Blob, classify_nonlinear_norm, &fx_info, &bl_features); if (sample == NULL) { delete Results; return NULL; } CharNormClassifier(Blob, *sample, Results); delete sample; RemoveBadMatches(Results); Results->match.sort(CompareByRating); /* copy the class id's into an string of ambiguities - don't copy if the correct class is the only class id matched */ Ambiguities = new UNICHAR_ID[Results->match.size() + 1]; if (Results->match.size() > 1 || (Results->match.size() == 1 && Results->match[0].unichar_id != CorrectClass)) { for (i = 0; i < Results->match.size(); i++) Ambiguities[i] = Results->match[i].unichar_id; Ambiguities[i] = -1; } else { Ambiguities[0] = -1; } delete Results; return Ambiguities; } /* GetAmbiguities */
int tesseract::Classify::GetCharNormFeature | ( | const INT_FX_RESULT_STRUCT & | fx_info, |
INT_TEMPLATES | templates, | ||
uinT8 * | pruner_norm_array, | ||
uinT8 * | char_norm_array | ||
) |
This routine calls the integer (Hardware) feature extractor if it has not been called before for this blob.
The results from the feature extractor are placed into globals so that they can be used in other routines without re-extracting the features.
It then copies the char norm features into the IntFeatures array provided by the caller.
Blob | blob to extract features from |
Templates | used to compute char norm adjustments |
IntFeatures | array to fill with integer features |
PrunerNormArray | Array of factors from blob normalization process |
CharNormArray | array to fill with dummy char norm adjustments |
BlobLength | length of blob in baseline-normalized units |
Globals:
Definition at line 1754 of file adaptmatch.cpp.
{ FEATURE norm_feature = NewFeature(&CharNormDesc); float baseline = kBlnBaselineOffset; float scale = MF_SCALE_FACTOR; norm_feature->Params[CharNormY] = (fx_info.Ymean - baseline) * scale; norm_feature->Params[CharNormLength] = fx_info.Length * scale / LENGTH_COMPRESSION; norm_feature->Params[CharNormRx] = fx_info.Rx * scale; norm_feature->Params[CharNormRy] = fx_info.Ry * scale; // Deletes norm_feature. ComputeCharNormArrays(norm_feature, templates, char_norm_array, pruner_norm_array); return IntCastRounded(fx_info.Length / kStandardFeatureLength); } /* GetCharNormFeature */
CLASS_ID tesseract::Classify::GetClassToDebug | ( | const char * | Prompt, |
bool * | adaptive_on, | ||
bool * | pretrained_on, | ||
int * | shape_id | ||
) |
Definition at line 1449 of file intproto.cpp.
{ /* ** Parameters: ** Prompt prompt to print while waiting for input from window ** Globals: none ** Operation: This routine prompts the user with Prompt and waits ** for the user to enter something in the debug window. ** Return: Character entered in the debug window. ** Exceptions: none ** History: Thu Mar 21 16:55:13 1991, DSJ, Created. */ tprintf("%s\n", Prompt); SVEvent* ev; SVEventType ev_type; int unichar_id = INVALID_UNICHAR_ID; // Wait until a click or popup event. do { ev = IntMatchWindow->AwaitEvent(SVET_ANY); ev_type = ev->type; if (ev_type == SVET_POPUP) { if (ev->command_id == IDA_SHAPE_INDEX) { if (shape_table_ != NULL) { *shape_id = atoi(ev->parameter); *adaptive_on = false; *pretrained_on = true; if (*shape_id >= 0 && *shape_id < shape_table_->NumShapes()) { int font_id; shape_table_->GetFirstUnicharAndFont(*shape_id, &unichar_id, &font_id); tprintf("Shape %d, first unichar=%d, font=%d\n", *shape_id, unichar_id, font_id); return unichar_id; } tprintf("Shape index '%s' not found in shape table\n", ev->parameter); } else { tprintf("No shape table loaded!\n"); } } else { if (unicharset.contains_unichar(ev->parameter)) { unichar_id = unicharset.unichar_to_id(ev->parameter); if (ev->command_id == IDA_ADAPTIVE) { *adaptive_on = true; *pretrained_on = false; *shape_id = -1; } else if (ev->command_id == IDA_STATIC) { *adaptive_on = false; *pretrained_on = true; } else { *adaptive_on = true; *pretrained_on = true; } if (ev->command_id == IDA_ADAPTIVE || shape_table_ == NULL) { *shape_id = -1; return unichar_id; } for (int s = 0; s < shape_table_->NumShapes(); ++s) { if (shape_table_->GetShape(s).ContainsUnichar(unichar_id)) { tprintf("%s\n", shape_table_->DebugStr(s).string()); } } } else { tprintf("Char class '%s' not found in unicharset", ev->parameter); } } } delete ev; } while (ev_type != SVET_CLICK); return 0; } /* GetClassToDebug */
Dict& tesseract::Classify::getDict | ( | ) | [inline] |
Definition at line 64 of file classify.h.
{
return dict_;
}
int tesseract::Classify::GetFontinfoId | ( | ADAPT_CLASS | Class, |
uinT8 | ConfigId | ||
) |
Definition at line 190 of file adaptive.cpp.
{ return (ConfigIsPermanent(Class, ConfigId) ? PermConfigFor(Class, ConfigId)->FontinfoId : TempConfigFor(Class, ConfigId)->FontinfoId); }
void tesseract::Classify::InitAdaptedClass | ( | TBLOB * | Blob, |
CLASS_ID | ClassId, | ||
int | FontinfoId, | ||
ADAPT_CLASS | Class, | ||
ADAPT_TEMPLATES | Templates | ||
) |
This routine creates a new adapted class and uses Blob as the model for the first config in that class.
Blob | blob to model new class after |
ClassId | id of the class to be initialized |
FontinfoId | font information inferred from pre-trained templates |
Class | adapted class to be initialized |
Templates | adapted templates to add new class to |
Globals:
Definition at line 670 of file adaptmatch.cpp.
{ FEATURE_SET Features; int Fid, Pid; FEATURE Feature; int NumFeatures; TEMP_PROTO TempProto; PROTO Proto; INT_CLASS IClass; TEMP_CONFIG Config; classify_norm_method.set_value(baseline); Features = ExtractOutlineFeatures(Blob); NumFeatures = Features->NumFeatures; if (NumFeatures > UNLIKELY_NUM_FEAT || NumFeatures <= 0) { FreeFeatureSet(Features); return; } Config = NewTempConfig(NumFeatures - 1, FontinfoId); TempConfigFor(Class, 0) = Config; /* this is a kludge to construct cutoffs for adapted templates */ if (Templates == AdaptedTemplates) BaselineCutoffs[ClassId] = CharNormCutoffs[ClassId]; IClass = ClassForClassId (Templates->Templates, ClassId); for (Fid = 0; Fid < Features->NumFeatures; Fid++) { Pid = AddIntProto (IClass); assert (Pid != NO_PROTO); Feature = Features->Features[Fid]; TempProto = NewTempProto (); Proto = &(TempProto->Proto); /* compute proto params - NOTE that Y_DIM_OFFSET must be used because ConvertProto assumes that the Y dimension varies from -0.5 to 0.5 instead of the -0.25 to 0.75 used in baseline normalization */ Proto->Angle = Feature->Params[OutlineFeatDir]; Proto->X = Feature->Params[OutlineFeatX]; Proto->Y = Feature->Params[OutlineFeatY] - Y_DIM_OFFSET; Proto->Length = Feature->Params[OutlineFeatLength]; FillABC(Proto); TempProto->ProtoId = Pid; SET_BIT (Config->Protos, Pid); ConvertProto(Proto, Pid, IClass); AddProtoToProtoPruner(Proto, Pid, IClass, classify_learning_debug_level >= 2); Class->TempProtos = push (Class->TempProtos, TempProto); } FreeFeatureSet(Features); AddIntConfig(IClass); ConvertConfig (AllProtosOn, 0, IClass); if (classify_learning_debug_level >= 1) { cprintf ("Added new class '%s' with class id %d and %d protos.\n", unicharset.id_to_unichar(ClassId), ClassId, NumFeatures); if (classify_learning_debug_level > 1) DisplayAdaptedChar(Blob, IClass); } if (IsEmptyAdaptedClass(Class)) (Templates->NumNonEmptyClasses)++; } /* InitAdaptedClass */
void tesseract::Classify::InitAdaptiveClassifier | ( | bool | load_pre_trained_templates | ) |
This routine reads in the training information needed by the adaptive classifier and saves it into global variables. Parameters: load_pre_trained_templates Indicates whether the pre-trained templates (inttemp, normproto and pffmtable components) should be lodaded. Should only be set to true if the necesary classifier components are present in the [lang].traineddata file. Globals: BuiltInTemplatesFile file to get built-in temps from BuiltInCutoffsFile file to get avg. feat per class from classify_use_pre_adapted_templates enables use of pre-adapted templates
Definition at line 503 of file adaptmatch.cpp.
{ if (!classify_enable_adaptive_matcher) return; if (AllProtosOn != NULL) EndAdaptiveClassifier(); // Don't leak with multiple inits. // If there is no language_data_path_prefix, the classifier will be // adaptive only. if (language_data_path_prefix.length() > 0 && load_pre_trained_templates) { ASSERT_HOST(tessdata_manager.SeekToStart(TESSDATA_INTTEMP)); PreTrainedTemplates = ReadIntTemplates(tessdata_manager.GetDataFilePtr()); if (tessdata_manager.DebugLevel() > 0) tprintf("Loaded inttemp\n"); if (tessdata_manager.SeekToStart(TESSDATA_SHAPE_TABLE)) { shape_table_ = new ShapeTable(unicharset); if (!shape_table_->DeSerialize(tessdata_manager.swap(), tessdata_manager.GetDataFilePtr())) { tprintf("Error loading shape table!\n"); delete shape_table_; shape_table_ = NULL; } else if (tessdata_manager.DebugLevel() > 0) { tprintf("Successfully loaded shape table!\n"); } } ASSERT_HOST(tessdata_manager.SeekToStart(TESSDATA_PFFMTABLE)); ReadNewCutoffs(tessdata_manager.GetDataFilePtr(), tessdata_manager.swap(), tessdata_manager.GetEndOffset(TESSDATA_PFFMTABLE), CharNormCutoffs); if (tessdata_manager.DebugLevel() > 0) tprintf("Loaded pffmtable\n"); ASSERT_HOST(tessdata_manager.SeekToStart(TESSDATA_NORMPROTO)); NormProtos = ReadNormProtos(tessdata_manager.GetDataFilePtr(), tessdata_manager.GetEndOffset(TESSDATA_NORMPROTO)); if (tessdata_manager.DebugLevel() > 0) tprintf("Loaded normproto\n"); static_classifier_ = new TessClassifier(false, this); } im_.Init(&classify_debug_level); InitIntegerFX(); AllProtosOn = NewBitVector(MAX_NUM_PROTOS); AllConfigsOn = NewBitVector(MAX_NUM_CONFIGS); AllConfigsOff = NewBitVector(MAX_NUM_CONFIGS); TempProtoMask = NewBitVector(MAX_NUM_PROTOS); set_all_bits(AllProtosOn, WordsInVectorOfSize(MAX_NUM_PROTOS)); set_all_bits(AllConfigsOn, WordsInVectorOfSize(MAX_NUM_CONFIGS)); zero_all_bits(AllConfigsOff, WordsInVectorOfSize(MAX_NUM_CONFIGS)); for (int i = 0; i < MAX_NUM_CLASSES; i++) { BaselineCutoffs[i] = 0; } if (classify_use_pre_adapted_templates) { FILE *File; STRING Filename; Filename = imagefile; Filename += ADAPT_TEMPLATE_SUFFIX; File = fopen(Filename.string(), "rb"); if (File == NULL) { AdaptedTemplates = NewAdaptedTemplates(true); } else { #ifndef SECURE_NAMES cprintf("\nReading pre-adapted templates from %s ...\n", Filename.string()); fflush(stdout); #endif AdaptedTemplates = ReadAdaptedTemplates(File); cprintf("\n"); fclose(File); PrintAdaptedTemplates(stdout, AdaptedTemplates); for (int i = 0; i < AdaptedTemplates->Templates->NumClasses; i++) { BaselineCutoffs[i] = CharNormCutoffs[i]; } } } else { if (AdaptedTemplates != NULL) free_adapted_templates(AdaptedTemplates); AdaptedTemplates = NewAdaptedTemplates(true); } } /* InitAdaptiveClassifier */
bool tesseract::Classify::LargeSpeckle | ( | const TBLOB & | blob | ) |
Definition at line 226 of file classify.cpp.
{ double speckle_size = kBlnXHeight * speckle_large_max_size; TBOX bbox = blob.bounding_box(); return bbox.width() < speckle_size && bbox.height() < speckle_size; }
void tesseract::Classify::LearnPieces | ( | const char * | filename, |
int | start, | ||
int | length, | ||
float | threshold, | ||
CharSegmentationType | segmentation, | ||
const char * | correct_text, | ||
WERD_RES * | word | ||
) |
Definition at line 352 of file adaptmatch.cpp.
{ // TODO(daria) Remove/modify this if/when we want // to train and/or adapt to n-grams. if (segmentation != CST_WHOLE && (segmentation != CST_FRAGMENT || disable_character_fragments)) return; if (length > 1) { join_pieces(word->seam_array, start, start + length - 1, word->chopped_word); } TBLOB* blob = word->chopped_word->blobs[start]; // Rotate the blob if needed for classification. TBLOB* rotated_blob = blob->ClassifyNormalizeIfNeeded(); if (rotated_blob == NULL) rotated_blob = blob; #ifndef GRAPHICS_DISABLED // Draw debug windows showing the blob that is being learned if needed. if (strcmp(classify_learn_debug_str.string(), correct_text) == 0) { RefreshDebugWindow(&learn_debug_win_, "LearnPieces", 600, word->chopped_word->bounding_box()); rotated_blob->plot(learn_debug_win_, ScrollView::GREEN, ScrollView::BROWN); learn_debug_win_->Update(); window_wait(learn_debug_win_); } if (classify_debug_character_fragments && segmentation == CST_FRAGMENT) { ASSERT_HOST(learn_fragments_debug_win_ != NULL); // set up in LearnWord blob->plot(learn_fragments_debug_win_, ScrollView::BLUE, ScrollView::BROWN); learn_fragments_debug_win_->Update(); } #endif // GRAPHICS_DISABLED if (filename != NULL) { classify_norm_method.set_value(character); // force char norm spc 30/11/93 tess_bn_matching.set_value(false); // turn it off tess_cn_matching.set_value(false); DENORM bl_denorm, cn_denorm; INT_FX_RESULT_STRUCT fx_info; SetupBLCNDenorms(*rotated_blob, classify_nonlinear_norm, &bl_denorm, &cn_denorm, &fx_info); LearnBlob(feature_defs_, filename, rotated_blob, bl_denorm, cn_denorm, fx_info, correct_text); } else if (unicharset.contains_unichar(correct_text)) { UNICHAR_ID class_id = unicharset.unichar_to_id(correct_text); int font_id = word->fontinfo != NULL ? fontinfo_table_.get_id(*word->fontinfo) : 0; if (classify_learning_debug_level >= 1) tprintf("Adapting to char = %s, thr= %g font_id= %d\n", unicharset.id_to_unichar(class_id), threshold, font_id); // If filename is not NULL we are doing recognition // (as opposed to training), so we must have already set word fonts. AdaptToChar(rotated_blob, class_id, font_id, threshold); } else if (classify_debug_level >= 1) { tprintf("Can't adapt to %s not in unicharset\n", correct_text); } if (rotated_blob != blob) { delete rotated_blob; } break_pieces(word->seam_array, start, start + length - 1, word->chopped_word); } // LearnPieces.
void tesseract::Classify::LearnWord | ( | const char * | filename, |
WERD_RES * | word | ||
) |
Definition at line 228 of file adaptmatch.cpp.
{ int word_len = word->correct_text.size(); if (word_len == 0) return; float* thresholds = NULL; if (filename == NULL) { // Adaption mode. if (!EnableLearning || word->best_choice == NULL) return; // Can't or won't adapt. if (classify_learning_debug_level >= 1) tprintf("\n\nAdapting to word = %s\n", word->best_choice->debug_string().string()); thresholds = new float[word_len]; word->ComputeAdaptionThresholds(certainty_scale, matcher_perfect_threshold, matcher_good_threshold, matcher_rating_margin, thresholds); } int start_blob = 0; #ifndef GRAPHICS_DISABLED if (classify_debug_character_fragments) { if (learn_fragmented_word_debug_win_ != NULL) { window_wait(learn_fragmented_word_debug_win_); } RefreshDebugWindow(&learn_fragments_debug_win_, "LearnPieces", 400, word->chopped_word->bounding_box()); RefreshDebugWindow(&learn_fragmented_word_debug_win_, "LearnWord", 200, word->chopped_word->bounding_box()); word->chopped_word->plot(learn_fragmented_word_debug_win_); ScrollView::Update(); } #endif // GRAPHICS_DISABLED for (int ch = 0; ch < word_len; ++ch) { if (classify_debug_character_fragments) { tprintf("\nLearning %s\n", word->correct_text[ch].string()); } if (word->correct_text[ch].length() > 0) { float threshold = thresholds != NULL ? thresholds[ch] : 0.0f; LearnPieces(filename, start_blob, word->best_state[ch], threshold, CST_WHOLE, word->correct_text[ch].string(), word); if (word->best_state[ch] > 1 && !disable_character_fragments) { // Check that the character breaks into meaningful fragments // that each match a whole character with at least // classify_character_fragments_garbage_certainty_threshold bool garbage = false; int frag; for (frag = 0; frag < word->best_state[ch]; ++frag) { TBLOB* frag_blob = word->chopped_word->blobs[start_blob + frag]; if (classify_character_fragments_garbage_certainty_threshold < 0) { garbage |= LooksLikeGarbage(frag_blob); } } // Learn the fragments. if (!garbage) { bool pieces_all_natural = word->PiecesAllNatural(start_blob, word->best_state[ch]); if (pieces_all_natural || !prioritize_division) { for (frag = 0; frag < word->best_state[ch]; ++frag) { GenericVector<STRING> tokens; word->correct_text[ch].split(' ', &tokens); tokens[0] = CHAR_FRAGMENT::to_string( tokens[0].string(), frag, word->best_state[ch], pieces_all_natural); STRING full_string; for (int i = 0; i < tokens.size(); i++) { full_string += tokens[i]; if (i != tokens.size() - 1) full_string += ' '; } LearnPieces(filename, start_blob + frag, 1, threshold, CST_FRAGMENT, full_string.string(), word); } } } } // TODO(rays): re-enable this part of the code when we switch to the // new classifier that needs to see examples of garbage. /* if (word->best_state[ch] > 1) { // If the next blob is good, make junk with the rightmost fragment. if (ch + 1 < word_len && word->correct_text[ch + 1].length() > 0) { LearnPieces(filename, start_blob + word->best_state[ch] - 1, word->best_state[ch + 1] + 1, threshold, CST_IMPROPER, INVALID_UNICHAR, word); } // If the previous blob is good, make junk with the leftmost fragment. if (ch > 0 && word->correct_text[ch - 1].length() > 0) { LearnPieces(filename, start_blob - word->best_state[ch - 1], word->best_state[ch - 1] + 1, threshold, CST_IMPROPER, INVALID_UNICHAR, word); } } // If the next blob is good, make a join with it. if (ch + 1 < word_len && word->correct_text[ch + 1].length() > 0) { STRING joined_text = word->correct_text[ch]; joined_text += word->correct_text[ch + 1]; LearnPieces(filename, start_blob, word->best_state[ch] + word->best_state[ch + 1], threshold, CST_NGRAM, joined_text.string(), word); } */ } start_blob += word->best_state[ch]; } delete [] thresholds; } // LearnWord.
bool tesseract::Classify::LooksLikeGarbage | ( | TBLOB * | blob | ) |
Definition at line 1705 of file adaptmatch.cpp.
{ BLOB_CHOICE_LIST *ratings = new BLOB_CHOICE_LIST(); AdaptiveClassifier(blob, ratings); BLOB_CHOICE_IT ratings_it(ratings); const UNICHARSET &unicharset = getDict().getUnicharset(); if (classify_debug_character_fragments) { print_ratings_list("======================\nLooksLikeGarbage() got ", ratings, unicharset); } for (ratings_it.mark_cycle_pt(); !ratings_it.cycled_list(); ratings_it.forward()) { if (unicharset.get_fragment(ratings_it.data()->unichar_id()) != NULL) { continue; } float certainty = ratings_it.data()->certainty(); delete ratings; return certainty < classify_character_fragments_garbage_certainty_threshold; } delete ratings; return true; // no whole characters in ratings }
int tesseract::Classify::MakeNewTemporaryConfig | ( | ADAPT_TEMPLATES | Templates, |
CLASS_ID | ClassId, | ||
int | FontinfoId, | ||
int | NumFeatures, | ||
INT_FEATURE_ARRAY | Features, | ||
FEATURE_SET | FloatFeatures | ||
) |
Templates | adapted templates to add new config to |
ClassId | class id to associate with new config |
FontinfoId | font information inferred from pre-trained templates |
NumFeatures | number of features in IntFeatures |
Features | features describing model for new config |
FloatFeatures | floating-pt representation of features |
Definition at line 1818 of file adaptmatch.cpp.
{ INT_CLASS IClass; ADAPT_CLASS Class; PROTO_ID OldProtos[MAX_NUM_PROTOS]; FEATURE_ID BadFeatures[MAX_NUM_INT_FEATURES]; int NumOldProtos; int NumBadFeatures; int MaxProtoId, OldMaxProtoId; int BlobLength = 0; int MaskSize; int ConfigId; TEMP_CONFIG Config; int i; int debug_level = NO_DEBUG; if (classify_learning_debug_level >= 3) debug_level = PRINT_MATCH_SUMMARY | PRINT_FEATURE_MATCHES | PRINT_PROTO_MATCHES; IClass = ClassForClassId(Templates->Templates, ClassId); Class = Templates->Class[ClassId]; if (IClass->NumConfigs >= MAX_NUM_CONFIGS) { ++NumAdaptationsFailed; if (classify_learning_debug_level >= 1) cprintf("Cannot make new temporary config: maximum number exceeded.\n"); return -1; } OldMaxProtoId = IClass->NumProtos - 1; NumOldProtos = im_.FindGoodProtos(IClass, AllProtosOn, AllConfigsOff, BlobLength, NumFeatures, Features, OldProtos, classify_adapt_proto_threshold, debug_level); MaskSize = WordsInVectorOfSize(MAX_NUM_PROTOS); zero_all_bits(TempProtoMask, MaskSize); for (i = 0; i < NumOldProtos; i++) SET_BIT(TempProtoMask, OldProtos[i]); NumBadFeatures = im_.FindBadFeatures(IClass, TempProtoMask, AllConfigsOn, BlobLength, NumFeatures, Features, BadFeatures, classify_adapt_feature_threshold, debug_level); MaxProtoId = MakeNewTempProtos(FloatFeatures, NumBadFeatures, BadFeatures, IClass, Class, TempProtoMask); if (MaxProtoId == NO_PROTO) { ++NumAdaptationsFailed; if (classify_learning_debug_level >= 1) cprintf("Cannot make new temp protos: maximum number exceeded.\n"); return -1; } ConfigId = AddIntConfig(IClass); ConvertConfig(TempProtoMask, ConfigId, IClass); Config = NewTempConfig(MaxProtoId, FontinfoId); TempConfigFor(Class, ConfigId) = Config; copy_all_bits(TempProtoMask, Config->Protos, Config->ProtoVectorSize); if (classify_learning_debug_level >= 1) cprintf("Making new temp config %d fontinfo id %d" " using %d old and %d new protos.\n", ConfigId, Config->FontinfoId, NumOldProtos, MaxProtoId - OldMaxProtoId); return ConfigId; } /* MakeNewTemporaryConfig */
PROTO_ID tesseract::Classify::MakeNewTempProtos | ( | FEATURE_SET | Features, |
int | NumBadFeat, | ||
FEATURE_ID | BadFeat[], | ||
INT_CLASS | IClass, | ||
ADAPT_CLASS | Class, | ||
BIT_VECTOR | TempProtoMask | ||
) |
This routine finds sets of sequential bad features that all have the same angle and converts each set into a new temporary proto. The temp proto is added to the proto pruner for IClass, pushed onto the list of temp protos in Class, and added to TempProtoMask.
Features | floating-pt features describing new character |
NumBadFeat | number of bad features to turn into protos |
BadFeat | feature id's of bad features |
IClass | integer class templates to add new protos to |
Class | adapted class templates to add new protos to |
TempProtoMask | proto mask to add new protos to |
Globals: none
Definition at line 1915 of file adaptmatch.cpp.
{ FEATURE_ID *ProtoStart; FEATURE_ID *ProtoEnd; FEATURE_ID *LastBad; TEMP_PROTO TempProto; PROTO Proto; FEATURE F1, F2; FLOAT32 X1, X2, Y1, Y2; FLOAT32 A1, A2, AngleDelta; FLOAT32 SegmentLength; PROTO_ID Pid; for (ProtoStart = BadFeat, LastBad = ProtoStart + NumBadFeat; ProtoStart < LastBad; ProtoStart = ProtoEnd) { F1 = Features->Features[*ProtoStart]; X1 = F1->Params[PicoFeatX]; Y1 = F1->Params[PicoFeatY]; A1 = F1->Params[PicoFeatDir]; for (ProtoEnd = ProtoStart + 1, SegmentLength = GetPicoFeatureLength(); ProtoEnd < LastBad; ProtoEnd++, SegmentLength += GetPicoFeatureLength()) { F2 = Features->Features[*ProtoEnd]; X2 = F2->Params[PicoFeatX]; Y2 = F2->Params[PicoFeatY]; A2 = F2->Params[PicoFeatDir]; AngleDelta = fabs(A1 - A2); if (AngleDelta > 0.5) AngleDelta = 1.0 - AngleDelta; if (AngleDelta > matcher_clustering_max_angle_delta || fabs(X1 - X2) > SegmentLength || fabs(Y1 - Y2) > SegmentLength) break; } F2 = Features->Features[*(ProtoEnd - 1)]; X2 = F2->Params[PicoFeatX]; Y2 = F2->Params[PicoFeatY]; A2 = F2->Params[PicoFeatDir]; Pid = AddIntProto(IClass); if (Pid == NO_PROTO) return (NO_PROTO); TempProto = NewTempProto(); Proto = &(TempProto->Proto); /* compute proto params - NOTE that Y_DIM_OFFSET must be used because ConvertProto assumes that the Y dimension varies from -0.5 to 0.5 instead of the -0.25 to 0.75 used in baseline normalization */ Proto->Length = SegmentLength; Proto->Angle = A1; Proto->X = (X1 + X2) / 2.0; Proto->Y = (Y1 + Y2) / 2.0 - Y_DIM_OFFSET; FillABC(Proto); TempProto->ProtoId = Pid; SET_BIT(TempProtoMask, Pid); ConvertProto(Proto, Pid, IClass); AddProtoToProtoPruner(Proto, Pid, IClass, classify_learning_debug_level >= 2); Class->TempProtos = push(Class->TempProtos, TempProto); } return IClass->NumProtos - 1; } /* MakeNewTempProtos */
void tesseract::Classify::MakePermanent | ( | ADAPT_TEMPLATES | Templates, |
CLASS_ID | ClassId, | ||
int | ConfigId, | ||
TBLOB * | Blob | ||
) |
Templates | current set of adaptive templates |
ClassId | class containing config to be made permanent |
ConfigId | config to be made permanent |
Blob | current blob being adapted to |
Globals: none
Definition at line 2004 of file adaptmatch.cpp.
{ UNICHAR_ID *Ambigs; TEMP_CONFIG Config; ADAPT_CLASS Class; PROTO_KEY ProtoKey; Class = Templates->Class[ClassId]; Config = TempConfigFor(Class, ConfigId); MakeConfigPermanent(Class, ConfigId); if (Class->NumPermConfigs == 0) Templates->NumPermClasses++; Class->NumPermConfigs++; // Initialize permanent config. Ambigs = GetAmbiguities(Blob, ClassId); PERM_CONFIG Perm = (PERM_CONFIG) alloc_struct(sizeof(PERM_CONFIG_STRUCT), "PERM_CONFIG_STRUCT"); Perm->Ambigs = Ambigs; Perm->FontinfoId = Config->FontinfoId; // Free memory associated with temporary config (since ADAPTED_CONFIG // is a union we need to clean up before we record permanent config). ProtoKey.Templates = Templates; ProtoKey.ClassId = ClassId; ProtoKey.ConfigId = ConfigId; Class->TempProtos = delete_d(Class->TempProtos, &ProtoKey, MakeTempProtoPerm); FreeTempConfig(Config); // Record permanent config. PermConfigFor(Class, ConfigId) = Perm; if (classify_learning_debug_level >= 1) { tprintf("Making config %d for %s (ClassId %d) permanent:" " fontinfo id %d, ambiguities '", ConfigId, getDict().getUnicharset().debug_str(ClassId).string(), ClassId, PermConfigFor(Class, ConfigId)->FontinfoId); for (UNICHAR_ID *AmbigsPointer = Ambigs; *AmbigsPointer >= 0; ++AmbigsPointer) tprintf("%s", unicharset.id_to_unichar(*AmbigsPointer)); tprintf("'.\n"); } } /* MakePermanent */
void tesseract::Classify::MasterMatcher | ( | INT_TEMPLATES | templates, |
inT16 | num_features, | ||
const INT_FEATURE_STRUCT * | features, | ||
const uinT8 * | norm_factors, | ||
ADAPT_CLASS * | classes, | ||
int | debug, | ||
int | matcher_multiplier, | ||
const TBOX & | blob_box, | ||
const GenericVector< CP_RESULT_STRUCT > & | results, | ||
ADAPT_RESULTS * | final_results | ||
) |
Factored-out calls to IntegerMatcher based on class pruner results. Returns integer matcher results inside CLASS_PRUNER_RESULTS structure.
Definition at line 1099 of file adaptmatch.cpp.
{ int top = blob_box.top(); int bottom = blob_box.bottom(); for (int c = 0; c < results.size(); c++) { CLASS_ID class_id = results[c].Class; INT_RESULT_STRUCT& int_result = results[c].IMResult; BIT_VECTOR protos = classes != NULL ? classes[class_id]->PermProtos : AllProtosOn; BIT_VECTOR configs = classes != NULL ? classes[class_id]->PermConfigs : AllConfigsOn; im_.Match(ClassForClassId(templates, class_id), protos, configs, num_features, features, &int_result, classify_adapt_feature_threshold, debug, matcher_debug_separate_windows); bool debug = matcher_debug_level >= 2 || classify_debug_level > 1; ExpandShapesAndApplyCorrections(classes, debug, class_id, bottom, top, results[c].Rating, final_results->BlobLength, matcher_multiplier, norm_factors, int_result, final_results); } }
ADAPT_TEMPLATES tesseract::Classify::NewAdaptedTemplates | ( | bool | InitFromUnicharset | ) |
Allocates memory for adapted tempates. each char in unicharset to the newly created templates
InitFromUnicharset | if true, add an empty class for |
Definition at line 167 of file adaptive.cpp.
{ ADAPT_TEMPLATES Templates; int i; Templates = (ADAPT_TEMPLATES) Emalloc (sizeof (ADAPT_TEMPLATES_STRUCT)); Templates->Templates = NewIntTemplates (); Templates->NumPermClasses = 0; Templates->NumNonEmptyClasses = 0; /* Insert an empty class for each unichar id in unicharset */ for (i = 0; i < MAX_NUM_CLASSES; i++) { Templates->Class[i] = NULL; if (InitFromUnicharset && i < unicharset.size()) { AddAdaptedClass(Templates, NewAdaptedClass(), i); } } return (Templates); } /* NewAdaptedTemplates */
void tesseract::Classify::NormalizeOutlines | ( | LIST | Outlines, |
FLOAT32 * | XScale, | ||
FLOAT32 * | YScale | ||
) |
Definition at line 297 of file mfoutline.cpp.
{ /* ** Parameters: ** Outlines list of outlines to be normalized ** XScale x-direction scale factor used by routine ** YScale y-direction scale factor used by routine ** Globals: ** classify_norm_method method being used for normalization ** classify_char_norm_range map radius of gyration to this value ** Operation: This routine normalizes every outline in Outlines ** according to the currently selected normalization method. ** It also returns the scale factors that it used to do this ** scaling. The scale factors returned represent the x and ** y sizes in the normalized coordinate system that correspond ** to 1 pixel in the original coordinate system. ** Return: none (Outlines are changed and XScale and YScale are updated) ** Exceptions: none ** History: Fri Dec 14 08:14:55 1990, DSJ, Created. */ MFOUTLINE Outline; switch (classify_norm_method) { case character: ASSERT_HOST(!"How did NormalizeOutlines get called in character mode?"); break; case baseline: iterate(Outlines) { Outline = (MFOUTLINE) first_node(Outlines); NormalizeOutline(Outline, 0.0); } *XScale = *YScale = MF_SCALE_FACTOR; break; } } /* NormalizeOutlines */
void tesseract::Classify::PrintAdaptedTemplates | ( | FILE * | File, |
ADAPT_TEMPLATES | Templates | ||
) |
This routine prints a summary of the adapted templates in Templates to File.
Definition at line 273 of file adaptive.cpp.
{ int i; INT_CLASS IClass; ADAPT_CLASS AClass; #ifndef SECURE_NAMES fprintf (File, "\n\nSUMMARY OF ADAPTED TEMPLATES:\n\n"); fprintf (File, "Num classes = %d; Num permanent classes = %d\n\n", Templates->NumNonEmptyClasses, Templates->NumPermClasses); fprintf (File, " Id NC NPC NP NPP\n"); fprintf (File, "------------------------\n"); for (i = 0; i < (Templates->Templates)->NumClasses; i++) { IClass = Templates->Templates->Class[i]; AClass = Templates->Class[i]; if (!IsEmptyAdaptedClass (AClass)) { fprintf (File, "%5d %s %3d %3d %3d %3d\n", i, unicharset.id_to_unichar(i), IClass->NumConfigs, AClass->NumPermConfigs, IClass->NumProtos, IClass->NumProtos - count (AClass->TempProtos)); } } #endif fprintf (File, "\n"); } /* PrintAdaptedTemplates */
void tesseract::Classify::PrintAdaptiveMatchResults | ( | FILE * | File, |
ADAPT_RESULTS * | Results | ||
) |
This routine writes the matches in Results to File.
Globals: none
Definition at line 2104 of file adaptmatch.cpp.
int tesseract::Classify::PruneClasses | ( | const INT_TEMPLATES_STRUCT * | int_templates, |
int | num_features, | ||
const INT_FEATURE_STRUCT * | features, | ||
const uinT8 * | normalization_factors, | ||
const uinT16 * | expected_num_features, | ||
GenericVector< CP_RESULT_STRUCT > * | results | ||
) |
Definition at line 408 of file intmatcher.cpp.
{ /* ** Operation: ** Prunes the classes using a modified fast match table. ** Returns a sorted list of classes along with the number ** of pruned classes in that list. ** Return: Number of pruned classes. ** Exceptions: none ** History: Tue Feb 19 10:24:24 MST 1991, RWM, Created. */ ClassPruner pruner(int_templates->NumClasses); // Compute initial match scores for all classes. pruner.ComputeScores(int_templates, num_features, features); // Adjust match scores for number of expected features. pruner.AdjustForExpectedNumFeatures(expected_num_features, classify_cp_cutoff_strength); // Apply disabled classes in unicharset - only works without a shape_table. if (shape_table_ == NULL) pruner.DisableDisabledClasses(unicharset); // If fragments are disabled, remove them, also only without a shape table. if (disable_character_fragments && shape_table_ == NULL) pruner.DisableFragments(unicharset); // If we have good x-heights, apply the given normalization factors. if (normalization_factors != NULL) { pruner.NormalizeForXheight(classify_class_pruner_multiplier, normalization_factors); } else { pruner.NoNormalization(); } // Do the actual pruning and sort the short-list. pruner.PruneAndSort(classify_class_pruner_threshold, shape_table_ == NULL, unicharset); if (classify_debug_level > 2) { pruner.DebugMatch(*this, int_templates, features); } if (classify_debug_level > 1) { pruner.SummarizeResult(*this, int_templates, expected_num_features, classify_class_pruner_multiplier, normalization_factors); } // Convert to the expected output format. return pruner.SetupResults(results); }
ADAPT_TEMPLATES tesseract::Classify::ReadAdaptedTemplates | ( | FILE * | File | ) |
Read a set of adapted templates from File and return a ptr to the templates.
File | open text file to read adapted templates from |
Definition at line 371 of file adaptive.cpp.
{ int i; ADAPT_TEMPLATES Templates; /* first read the high level adaptive template struct */ Templates = (ADAPT_TEMPLATES) Emalloc (sizeof (ADAPT_TEMPLATES_STRUCT)); fread ((char *) Templates, sizeof (ADAPT_TEMPLATES_STRUCT), 1, File); /* then read in the basic integer templates */ Templates->Templates = ReadIntTemplates (File); /* then read in the adaptive info for each class */ for (i = 0; i < (Templates->Templates)->NumClasses; i++) { Templates->Class[i] = ReadAdaptedClass (File); } return (Templates); } /* ReadAdaptedTemplates */
INT_TEMPLATES tesseract::Classify::ReadIntTemplates | ( | FILE * | File | ) |
Definition at line 803 of file intproto.cpp.
{ /* ** Parameters: ** File open file to read templates from ** Globals: none ** Operation: This routine reads a set of integer templates from ** File. File must already be open and must be in the ** correct binary format. ** Return: Pointer to integer templates read from File. ** Exceptions: none ** History: Wed Feb 27 11:48:46 1991, DSJ, Created. */ int i, j, w, x, y, z; BOOL8 swap; int nread; int unicharset_size; int version_id = 0; INT_TEMPLATES Templates; CLASS_PRUNER_STRUCT* Pruner; INT_CLASS Class; uinT8 *Lengths; PROTO_SET ProtoSet; /* variables for conversion from older inttemp formats */ int b, bit_number, last_cp_bit_number, new_b, new_i, new_w; CLASS_ID class_id, max_class_id; inT16 *IndexFor = new inT16[MAX_NUM_CLASSES]; CLASS_ID *ClassIdFor = new CLASS_ID[MAX_NUM_CLASSES]; CLASS_PRUNER_STRUCT **TempClassPruner = new CLASS_PRUNER_STRUCT*[MAX_NUM_CLASS_PRUNERS]; uinT32 SetBitsForMask = // word with NUM_BITS_PER_CLASS (1 << NUM_BITS_PER_CLASS) - 1; // set starting at bit 0 uinT32 Mask, NewMask, ClassBits; int MaxNumConfigs = MAX_NUM_CONFIGS; int WerdsPerConfigVec = WERDS_PER_CONFIG_VEC; /* first read the high level template struct */ Templates = NewIntTemplates(); // Read Templates in parts for 64 bit compatibility. if (fread(&unicharset_size, sizeof(int), 1, File) != 1) cprintf("Bad read of inttemp!\n"); if (fread(&Templates->NumClasses, sizeof(Templates->NumClasses), 1, File) != 1 || fread(&Templates->NumClassPruners, sizeof(Templates->NumClassPruners), 1, File) != 1) cprintf("Bad read of inttemp!\n"); // Swap status is determined automatically. swap = Templates->NumClassPruners < 0 || Templates->NumClassPruners > MAX_NUM_CLASS_PRUNERS; if (swap) { Reverse32(&Templates->NumClassPruners); Reverse32(&Templates->NumClasses); Reverse32(&unicharset_size); } if (Templates->NumClasses < 0) { // This file has a version id! version_id = -Templates->NumClasses; if (fread(&Templates->NumClasses, sizeof(Templates->NumClasses), 1, File) != 1) cprintf("Bad read of inttemp!\n"); if (swap) Reverse32(&Templates->NumClasses); } if (version_id < 3) { MaxNumConfigs = OLD_MAX_NUM_CONFIGS; WerdsPerConfigVec = OLD_WERDS_PER_CONFIG_VEC; } if (version_id < 2) { for (i = 0; i < unicharset_size; ++i) { if (fread(&IndexFor[i], sizeof(inT16), 1, File) != 1) cprintf("Bad read of inttemp!\n"); } for (i = 0; i < Templates->NumClasses; ++i) { if (fread(&ClassIdFor[i], sizeof(CLASS_ID), 1, File) != 1) cprintf("Bad read of inttemp!\n"); } if (swap) { for (i = 0; i < Templates->NumClasses; i++) Reverse16(&IndexFor[i]); for (i = 0; i < Templates->NumClasses; i++) Reverse32(&ClassIdFor[i]); } } /* then read in the class pruners */ for (i = 0; i < Templates->NumClassPruners; i++) { Pruner = new CLASS_PRUNER_STRUCT; if ((nread = fread(Pruner, 1, sizeof(CLASS_PRUNER_STRUCT), File)) != sizeof(CLASS_PRUNER_STRUCT)) cprintf("Bad read of inttemp!\n"); if (swap) { for (x = 0; x < NUM_CP_BUCKETS; x++) { for (y = 0; y < NUM_CP_BUCKETS; y++) { for (z = 0; z < NUM_CP_BUCKETS; z++) { for (w = 0; w < WERDS_PER_CP_VECTOR; w++) { Reverse32(&Pruner->p[x][y][z][w]); } } } } } if (version_id < 2) { TempClassPruner[i] = Pruner; } else { Templates->ClassPruners[i] = Pruner; } } /* fix class pruners if they came from an old version of inttemp */ if (version_id < 2) { // Allocate enough class pruners to cover all the class ids. max_class_id = 0; for (i = 0; i < Templates->NumClasses; i++) if (ClassIdFor[i] > max_class_id) max_class_id = ClassIdFor[i]; for (i = 0; i <= CPrunerIdFor(max_class_id); i++) { Templates->ClassPruners[i] = new CLASS_PRUNER_STRUCT; memset(Templates->ClassPruners[i], 0, sizeof(CLASS_PRUNER_STRUCT)); } // Convert class pruners from the old format (indexed by class index) // to the new format (indexed by class id). last_cp_bit_number = NUM_BITS_PER_CLASS * Templates->NumClasses - 1; for (i = 0; i < Templates->NumClassPruners; i++) { for (x = 0; x < NUM_CP_BUCKETS; x++) for (y = 0; y < NUM_CP_BUCKETS; y++) for (z = 0; z < NUM_CP_BUCKETS; z++) for (w = 0; w < WERDS_PER_CP_VECTOR; w++) { if (TempClassPruner[i]->p[x][y][z][w] == 0) continue; for (b = 0; b < BITS_PER_WERD; b += NUM_BITS_PER_CLASS) { bit_number = i * BITS_PER_CP_VECTOR + w * BITS_PER_WERD + b; if (bit_number > last_cp_bit_number) break; // the rest of the bits in this word are not used class_id = ClassIdFor[bit_number / NUM_BITS_PER_CLASS]; // Single out NUM_BITS_PER_CLASS bits relating to class_id. Mask = SetBitsForMask << b; ClassBits = TempClassPruner[i]->p[x][y][z][w] & Mask; // Move these bits to the new position in which they should // appear (indexed corresponding to the class_id). new_i = CPrunerIdFor(class_id); new_w = CPrunerWordIndexFor(class_id); new_b = CPrunerBitIndexFor(class_id) * NUM_BITS_PER_CLASS; if (new_b > b) { ClassBits <<= (new_b - b); } else { ClassBits >>= (b - new_b); } // Copy bits relating to class_id to the correct position // in Templates->ClassPruner. NewMask = SetBitsForMask << new_b; Templates->ClassPruners[new_i]->p[x][y][z][new_w] &= ~NewMask; Templates->ClassPruners[new_i]->p[x][y][z][new_w] |= ClassBits; } } } for (i = 0; i < Templates->NumClassPruners; i++) { delete TempClassPruner[i]; } } /* then read in each class */ for (i = 0; i < Templates->NumClasses; i++) { /* first read in the high level struct for the class */ Class = (INT_CLASS) Emalloc (sizeof (INT_CLASS_STRUCT)); if (fread(&Class->NumProtos, sizeof(Class->NumProtos), 1, File) != 1 || fread(&Class->NumProtoSets, sizeof(Class->NumProtoSets), 1, File) != 1 || fread(&Class->NumConfigs, sizeof(Class->NumConfigs), 1, File) != 1) cprintf ("Bad read of inttemp!\n"); if (version_id == 0) { // Only version 0 writes 5 pointless pointers to the file. for (j = 0; j < 5; ++j) { int junk; if (fread(&junk, sizeof(junk), 1, File) != 1) cprintf ("Bad read of inttemp!\n"); } } if (version_id < 4) { for (j = 0; j < MaxNumConfigs; ++j) { if (fread(&Class->ConfigLengths[j], sizeof(uinT16), 1, File) != 1) cprintf ("Bad read of inttemp!\n"); } if (swap) { Reverse16(&Class->NumProtos); for (j = 0; j < MaxNumConfigs; j++) Reverse16(&Class->ConfigLengths[j]); } } else { ASSERT_HOST(Class->NumConfigs < MaxNumConfigs); for (j = 0; j < Class->NumConfigs; ++j) { if (fread(&Class->ConfigLengths[j], sizeof(uinT16), 1, File) != 1) cprintf ("Bad read of inttemp!\n"); } if (swap) { Reverse16(&Class->NumProtos); for (j = 0; j < MaxNumConfigs; j++) Reverse16(&Class->ConfigLengths[j]); } } if (version_id < 2) { ClassForClassId (Templates, ClassIdFor[i]) = Class; } else { ClassForClassId (Templates, i) = Class; } /* then read in the proto lengths */ Lengths = NULL; if (MaxNumIntProtosIn (Class) > 0) { Lengths = (uinT8 *)Emalloc(sizeof(uinT8) * MaxNumIntProtosIn(Class)); if ((nread = fread((char *)Lengths, sizeof(uinT8), MaxNumIntProtosIn(Class), File)) != MaxNumIntProtosIn (Class)) cprintf ("Bad read of inttemp!\n"); } Class->ProtoLengths = Lengths; /* then read in the proto sets */ for (j = 0; j < Class->NumProtoSets; j++) { ProtoSet = (PROTO_SET)Emalloc(sizeof(PROTO_SET_STRUCT)); if (version_id < 3) { if ((nread = fread((char *) &ProtoSet->ProtoPruner, 1, sizeof(PROTO_PRUNER), File)) != sizeof(PROTO_PRUNER)) cprintf("Bad read of inttemp!\n"); for (x = 0; x < PROTOS_PER_PROTO_SET; x++) { if ((nread = fread((char *) &ProtoSet->Protos[x].A, 1, sizeof(inT8), File)) != sizeof(inT8) || (nread = fread((char *) &ProtoSet->Protos[x].B, 1, sizeof(uinT8), File)) != sizeof(uinT8) || (nread = fread((char *) &ProtoSet->Protos[x].C, 1, sizeof(inT8), File)) != sizeof(inT8) || (nread = fread((char *) &ProtoSet->Protos[x].Angle, 1, sizeof(uinT8), File)) != sizeof(uinT8)) cprintf("Bad read of inttemp!\n"); for (y = 0; y < WerdsPerConfigVec; y++) if ((nread = fread((char *) &ProtoSet->Protos[x].Configs[y], 1, sizeof(uinT32), File)) != sizeof(uinT32)) cprintf("Bad read of inttemp!\n"); } } else { if ((nread = fread((char *) ProtoSet, 1, sizeof(PROTO_SET_STRUCT), File)) != sizeof(PROTO_SET_STRUCT)) cprintf("Bad read of inttemp!\n"); } if (swap) { for (x = 0; x < NUM_PP_PARAMS; x++) for (y = 0; y < NUM_PP_BUCKETS; y++) for (z = 0; z < WERDS_PER_PP_VECTOR; z++) Reverse32(&ProtoSet->ProtoPruner[x][y][z]); for (x = 0; x < PROTOS_PER_PROTO_SET; x++) for (y = 0; y < WerdsPerConfigVec; y++) Reverse32(&ProtoSet->Protos[x].Configs[y]); } Class->ProtoSets[j] = ProtoSet; } if (version_id < 4) Class->font_set_id = -1; else { fread(&Class->font_set_id, sizeof(int), 1, File); if (swap) Reverse32(&Class->font_set_id); } } if (version_id < 2) { /* add an empty NULL class with class id 0 */ assert(UnusedClassIdIn (Templates, 0)); ClassForClassId (Templates, 0) = NewIntClass (1, 1); ClassForClassId (Templates, 0)->font_set_id = -1; Templates->NumClasses++; /* make sure the classes are contiguous */ for (i = 0; i < MAX_NUM_CLASSES; i++) { if (i < Templates->NumClasses) { if (ClassForClassId (Templates, i) == NULL) { fprintf(stderr, "Non-contiguous class ids in inttemp\n"); exit(1); } } else { if (ClassForClassId (Templates, i) != NULL) { fprintf(stderr, "Class id %d exceeds NumClassesIn (Templates) %d\n", i, Templates->NumClasses); exit(1); } } } } if (version_id >= 4) { this->fontinfo_table_.read(File, NewPermanentTessCallback(read_info), swap); if (version_id >= 5) { this->fontinfo_table_.read(File, NewPermanentTessCallback(read_spacing_info), swap); } this->fontset_table_.read(File, NewPermanentTessCallback(read_set), swap); } // Clean up. delete[] IndexFor; delete[] ClassIdFor; delete[] TempClassPruner; return (Templates); } /* ReadIntTemplates */
void tesseract::Classify::ReadNewCutoffs | ( | FILE * | CutoffFile, |
bool | swap, | ||
inT64 | end_offset, | ||
CLASS_CUTOFF_ARRAY | Cutoffs | ||
) |
Definition at line 42 of file cutoffs.cpp.
{ /* ** Parameters: ** Filename name of file containing cutoff definitions ** Cutoffs array to put cutoffs into ** Globals: none ** Operation: Open Filename, read in all of the class-id/cutoff pairs ** and insert them into the Cutoffs array. Cutoffs are ** indexed in the array by class id. Unused entries in the ** array are set to an arbitrarily high cutoff value. ** Return: none ** Exceptions: none ** History: Wed Feb 20 09:38:26 1991, DSJ, Created. */ char Class[UNICHAR_LEN + 1]; CLASS_ID ClassId; int Cutoff; int i; if (shape_table_ != NULL) { if (!shapetable_cutoffs_.DeSerialize(swap, CutoffFile)) { tprintf("Error during read of shapetable pffmtable!\n"); } } for (i = 0; i < MAX_NUM_CLASSES; i++) Cutoffs[i] = MAX_CUTOFF; while ((end_offset < 0 || ftell(CutoffFile) < end_offset) && fscanf(CutoffFile, "%" REALLY_QUOTE_IT(UNICHAR_LEN) "s %d", Class, &Cutoff) == 2) { if (strcmp(Class, "NULL") == 0) { ClassId = unicharset.unichar_to_id(" "); } else { ClassId = unicharset.unichar_to_id(Class); } Cutoffs[ClassId] = Cutoff; SkipNewline(CutoffFile); } } /* ReadNewCutoffs */
NORM_PROTOS * tesseract::Classify::ReadNormProtos | ( | FILE * | File, |
inT64 | end_offset | ||
) |
Definition at line 234 of file normmatch.cpp.
{ /* ** Parameters: ** File open text file to read normalization protos from ** Globals: none ** Operation: This routine allocates a new data structure to hold ** a set of character normalization protos. It then fills in ** the data structure by reading from the specified File. ** Return: Character normalization protos. ** Exceptions: none ** History: Wed Dec 19 16:38:49 1990, DSJ, Created. */ NORM_PROTOS *NormProtos; int i; char unichar[2 * UNICHAR_LEN + 1]; UNICHAR_ID unichar_id; LIST Protos; int NumProtos; /* allocate and initialization data structure */ NormProtos = (NORM_PROTOS *) Emalloc (sizeof (NORM_PROTOS)); NormProtos->NumProtos = unicharset.size(); NormProtos->Protos = (LIST *) Emalloc (NormProtos->NumProtos * sizeof(LIST)); for (i = 0; i < NormProtos->NumProtos; i++) NormProtos->Protos[i] = NIL_LIST; /* read file header and save in data structure */ NormProtos->NumParams = ReadSampleSize (File); NormProtos->ParamDesc = ReadParamDesc (File, NormProtos->NumParams); /* read protos for each class into a separate list */ while ((end_offset < 0 || ftell(File) < end_offset) && fscanf(File, "%s %d", unichar, &NumProtos) == 2) { if (unicharset.contains_unichar(unichar)) { unichar_id = unicharset.unichar_to_id(unichar); Protos = NormProtos->Protos[unichar_id]; for (i = 0; i < NumProtos; i++) Protos = push_last (Protos, ReadPrototype (File, NormProtos->NumParams)); NormProtos->Protos[unichar_id] = Protos; } else { cprintf("Error: unichar %s in normproto file is not in unichar set.\n", unichar); for (i = 0; i < NumProtos; i++) FreePrototype(ReadPrototype (File, NormProtos->NumParams)); } SkipNewline(File); } return (NormProtos); } /* ReadNormProtos */
void tesseract::Classify::RefreshDebugWindow | ( | ScrollView ** | win, |
const char * | msg, | ||
int | y_offset, | ||
const TBOX & | wbox | ||
) |
Definition at line 202 of file adaptmatch.cpp.
{ #ifndef GRAPHICS_DISABLED const int kSampleSpaceWidth = 500; if (*win == NULL) { *win = new ScrollView(msg, 100, y_offset, kSampleSpaceWidth * 2, 200, kSampleSpaceWidth * 2, 200, true); } (*win)->Clear(); (*win)->Pen(64, 64, 64); (*win)->Line(-kSampleSpaceWidth, kBlnBaselineOffset, kSampleSpaceWidth, kBlnBaselineOffset); (*win)->Line(-kSampleSpaceWidth, kBlnXHeight + kBlnBaselineOffset, kSampleSpaceWidth, kBlnXHeight + kBlnBaselineOffset); (*win)->ZoomToRectangle(wbox.left(), wbox.top(), wbox.right(), wbox.bottom()); #endif // GRAPHICS_DISABLED }
void tesseract::Classify::RemoveBadMatches | ( | ADAPT_RESULTS * | Results | ) |
This routine steps thru each matching class in Results and removes it from the match list if its rating is worse than the BestRating plus a pad. In other words, all good matches get moved to the front of the classes array.
Results | contains matches to be filtered |
Globals:
Definition at line 2130 of file adaptmatch.cpp.
{ int Next, NextGood; FLOAT32 BadMatchThreshold; static const char* romans = "i v x I V X"; BadMatchThreshold = Results->best_match.rating + matcher_bad_match_pad; if (classify_bln_numeric_mode) { UNICHAR_ID unichar_id_one = unicharset.contains_unichar("1") ? unicharset.unichar_to_id("1") : -1; UNICHAR_ID unichar_id_zero = unicharset.contains_unichar("0") ? unicharset.unichar_to_id("0") : -1; ScoredClass scored_one = ScoredUnichar(Results, unichar_id_one); ScoredClass scored_zero = ScoredUnichar(Results, unichar_id_zero); for (Next = NextGood = 0; Next < Results->match.size(); Next++) { if (Results->match[Next].rating <= BadMatchThreshold) { ScoredClass match = Results->match[Next]; if (!unicharset.get_isalpha(match.unichar_id) || strstr(romans, unicharset.id_to_unichar(match.unichar_id)) != NULL) { Results->match[NextGood++] = Results->match[Next]; } else if (unicharset.eq(match.unichar_id, "l") && scored_one.rating >= BadMatchThreshold) { Results->match[NextGood] = scored_one; Results->match[NextGood].rating = match.rating; NextGood++; } else if (unicharset.eq(match.unichar_id, "O") && scored_zero.rating >= BadMatchThreshold) { Results->match[NextGood] = scored_zero; Results->match[NextGood].rating = match.rating; NextGood++; } } } } else { for (Next = NextGood = 0; Next < Results->match.size(); Next++) { if (Results->match[Next].rating <= BadMatchThreshold) Results->match[NextGood++] = Results->match[Next]; } } Results->match.truncate(NextGood); } /* RemoveBadMatches */
void tesseract::Classify::RemoveExtraPuncs | ( | ADAPT_RESULTS * | Results | ) |
This routine discards extra digits or punctuation from the results. We keep only the top 2 punctuation answers and the top 1 digit answer if present.
Results | contains matches to be filtered |
Definition at line 2183 of file adaptmatch.cpp.
{ int Next, NextGood; int punc_count; /*no of garbage characters */ int digit_count; /*garbage characters */ static char punc_chars[] = ". , ; : / ` ~ ' - = \\ | \" ! _ ^"; static char digit_chars[] = "0 1 2 3 4 5 6 7 8 9"; punc_count = 0; digit_count = 0; for (Next = NextGood = 0; Next < Results->match.size(); Next++) { ScoredClass match = Results->match[Next]; if (strstr(punc_chars, unicharset.id_to_unichar(match.unichar_id)) != NULL) { if (punc_count < 2) Results->match[NextGood++] = match; punc_count++; } else { if (strstr(digit_chars, unicharset.id_to_unichar(match.unichar_id)) != NULL) { if (digit_count < 1) Results->match[NextGood++] = match; digit_count++; } else { Results->match[NextGood++] = match; } } } Results->match.truncate(NextGood); } /* RemoveExtraPuncs */
Definition at line 591 of file adaptmatch.cpp.
{ if (classify_learning_debug_level > 0) { tprintf("Resetting adaptive classifier (NumAdaptationsFailed=%d)\n", NumAdaptationsFailed); } free_adapted_templates(AdaptedTemplates); AdaptedTemplates = NewAdaptedTemplates(true); NumAdaptationsFailed = 0; }
void tesseract::Classify::SetAdaptiveThreshold | ( | FLOAT32 | Threshold | ) |
This routine resets the internal thresholds inside the integer matcher to correspond to the specified threshold.
Threshold | threshold for creating new templates |
Globals:
Definition at line 2228 of file adaptmatch.cpp.
{ Threshold = (Threshold == matcher_good_threshold) ? 0.9: (1.0 - Threshold); classify_adapt_proto_threshold.set_value( ClipToRange<int>(255 * Threshold, 0, 255)); classify_adapt_feature_threshold.set_value( ClipToRange<int>(255 * Threshold, 0, 255)); } /* SetAdaptiveThreshold */
void tesseract::Classify::SetStaticClassifier | ( | ShapeClassifier * | static_classifier | ) |
Definition at line 195 of file classify.cpp.
{
delete static_classifier_;
static_classifier_ = static_classifier;
}
void tesseract::Classify::SettupPass1 | ( | ) |
This routine prepares the adaptive matcher for the start of the first pass. Learning is enabled (unless it is disabled for the whole program).
Globals:
Definition at line 623 of file adaptmatch.cpp.
{ EnableLearning = classify_enable_learning; getDict().SettupStopperPass1(); } /* SettupPass1 */
void tesseract::Classify::SettupPass2 | ( | ) |
This routine prepares the adaptive matcher for the start of the second pass. Further learning is disabled.
Globals:
Definition at line 643 of file adaptmatch.cpp.
{ EnableLearning = FALSE; getDict().SettupStopperPass2(); } /* SettupPass2 */
void tesseract::Classify::SetupBLCNDenorms | ( | const TBLOB & | blob, |
bool | nonlinear_norm, | ||
DENORM * | bl_denorm, | ||
DENORM * | cn_denorm, | ||
INT_FX_RESULT_STRUCT * | fx_info | ||
) | [static] |
Definition at line 133 of file intfx.cpp.
{ // Compute 1st and 2nd moments of the original outline. FCOORD center, second_moments; int length = blob.ComputeMoments(¢er, &second_moments); if (fx_info != NULL) { fx_info->Length = length; fx_info->Rx = IntCastRounded(second_moments.y()); fx_info->Ry = IntCastRounded(second_moments.x()); fx_info->Xmean = IntCastRounded(center.x()); fx_info->Ymean = IntCastRounded(center.y()); } // Setup the denorm for Baseline normalization. bl_denorm->SetupNormalization(NULL, NULL, &blob.denorm(), center.x(), 128.0f, 1.0f, 1.0f, 128.0f, 128.0f); // Setup the denorm for character normalization. if (nonlinear_norm) { GenericVector<GenericVector<int> > x_coords; GenericVector<GenericVector<int> > y_coords; TBOX box; blob.GetPreciseBoundingBox(&box); box.pad(1, 1); blob.GetEdgeCoords(box, &x_coords, &y_coords); cn_denorm->SetupNonLinear(&blob.denorm(), box, MAX_UINT8, MAX_UINT8, 0.0f, 0.0f, x_coords, y_coords); } else { cn_denorm->SetupNormalization(NULL, NULL, &blob.denorm(), center.x(), center.y(), 51.2f / second_moments.x(), 51.2f / second_moments.y(), 128.0f, 128.0f); } }
const ShapeTable* tesseract::Classify::shape_table | ( | ) | const [inline] |
Definition at line 68 of file classify.h.
{ return shape_table_; }
int tesseract::Classify::ShapeIDToClassID | ( | int | shape_id | ) | const |
Definition at line 2312 of file adaptmatch.cpp.
{ for (int id = 0; id < PreTrainedTemplates->NumClasses; ++id) { int font_set_id = PreTrainedTemplates->Class[id]->font_set_id; ASSERT_HOST(font_set_id >= 0); const FontSet &fs = fontset_table_.get(font_set_id); for (int config = 0; config < fs.size; ++config) { if (fs.configs[config] == shape_id) return id; } } tprintf("Shape %d not found\n", shape_id); return -1; }
void tesseract::Classify::ShowBestMatchFor | ( | int | shape_id, |
const INT_FEATURE_STRUCT * | features, | ||
int | num_features | ||
) |
This routine displays debug information for the best config of the given shape_id for the given set of features.
shape_id | classifier id to work with |
features | features of the unknown character |
num_features | Number of features in the features array. |
Definition at line 2249 of file adaptmatch.cpp.
{ #ifndef GRAPHICS_DISABLED uinT32 config_mask; if (UnusedClassIdIn(PreTrainedTemplates, shape_id)) { tprintf("No built-in templates for class/shape %d\n", shape_id); return; } if (num_features <= 0) { tprintf("Illegal blob (char norm features)!\n"); return; } INT_RESULT_STRUCT cn_result; classify_norm_method.set_value(character); im_.Match(ClassForClassId(PreTrainedTemplates, shape_id), AllProtosOn, AllConfigsOn, num_features, features, &cn_result, classify_adapt_feature_threshold, NO_DEBUG, matcher_debug_separate_windows); tprintf("\n"); config_mask = 1 << cn_result.Config; tprintf("Static Shape ID: %d\n", shape_id); ShowMatchDisplay(); im_.Match(ClassForClassId(PreTrainedTemplates, shape_id), AllProtosOn, reinterpret_cast<BIT_VECTOR>(&config_mask), num_features, features, &cn_result, classify_adapt_feature_threshold, matcher_debug_flags, matcher_debug_separate_windows); UpdateMatchDisplay(); #endif // GRAPHICS_DISABLED } /* ShowBestMatchFor */
Definition at line 1113 of file intproto.cpp.
{ /* ** Parameters: none ** Globals: ** FeatureShapes display list containing feature matches ** ProtoShapes display list containing proto matches ** Operation: This routine sends the shapes in the global display ** lists to the match debugger window. ** Return: none ** Exceptions: none ** History: Thu Mar 21 15:47:33 1991, DSJ, Created. */ InitIntMatchWindowIfReqd(); if (ProtoDisplayWindow) { ProtoDisplayWindow->Clear(); } if (FeatureDisplayWindow) { FeatureDisplayWindow->Clear(); } ClearFeatureSpaceWindow( static_cast<NORM_METHOD>(static_cast<int>(classify_norm_method)), IntMatchWindow); IntMatchWindow->ZoomToRectangle(INT_MIN_X, INT_MIN_Y, INT_MAX_X, INT_MAX_Y); if (ProtoDisplayWindow) { ProtoDisplayWindow->ZoomToRectangle(INT_MIN_X, INT_MIN_Y, INT_MAX_X, INT_MAX_Y); } if (FeatureDisplayWindow) { FeatureDisplayWindow->ZoomToRectangle(INT_MIN_X, INT_MIN_Y, INT_MAX_X, INT_MAX_Y); } } /* ShowMatchDisplay */
bool tesseract::Classify::TempConfigReliable | ( | CLASS_ID | class_id, |
const TEMP_CONFIG & | config | ||
) |
Definition at line 2328 of file adaptmatch.cpp.
{ if (classify_learning_debug_level >= 1) { tprintf("NumTimesSeen for config of %s is %d\n", getDict().getUnicharset().debug_str(class_id).string(), config->NumTimesSeen); } if (config->NumTimesSeen >= matcher_sufficient_examples_for_prototyping) { return true; } else if (config->NumTimesSeen < matcher_min_examples_for_prototyping) { return false; } else if (use_ambigs_for_adaption) { // Go through the ambigs vector and see whether we have already seen // enough times all the characters represented by the ambigs vector. const UnicharIdVector *ambigs = getDict().getUnicharAmbigs().AmbigsForAdaption(class_id); int ambigs_size = (ambigs == NULL) ? 0 : ambigs->size(); for (int ambig = 0; ambig < ambigs_size; ++ambig) { ADAPT_CLASS ambig_class = AdaptedTemplates->Class[(*ambigs)[ambig]]; assert(ambig_class != NULL); if (ambig_class->NumPermConfigs == 0 && ambig_class->MaxNumTimesSeen < matcher_min_examples_for_prototyping) { if (classify_learning_debug_level >= 1) { tprintf("Ambig %s has not been seen enough times," " not making config for %s permanent\n", getDict().getUnicharset().debug_str( (*ambigs)[ambig]).string(), getDict().getUnicharset().debug_str(class_id).string()); } return false; } } } return true; }
void tesseract::Classify::UpdateAmbigsGroup | ( | CLASS_ID | class_id, |
TBLOB * | Blob | ||
) |
Definition at line 2365 of file adaptmatch.cpp.
{ const UnicharIdVector *ambigs = getDict().getUnicharAmbigs().ReverseAmbigsForAdaption(class_id); int ambigs_size = (ambigs == NULL) ? 0 : ambigs->size(); if (classify_learning_debug_level >= 1) { tprintf("Running UpdateAmbigsGroup for %s class_id=%d\n", getDict().getUnicharset().debug_str(class_id).string(), class_id); } for (int ambig = 0; ambig < ambigs_size; ++ambig) { CLASS_ID ambig_class_id = (*ambigs)[ambig]; const ADAPT_CLASS ambigs_class = AdaptedTemplates->Class[ambig_class_id]; for (int cfg = 0; cfg < MAX_NUM_CONFIGS; ++cfg) { if (ConfigIsPermanent(ambigs_class, cfg)) continue; const TEMP_CONFIG config = TempConfigFor(AdaptedTemplates->Class[ambig_class_id], cfg); if (config != NULL && TempConfigReliable(ambig_class_id, config)) { if (classify_learning_debug_level >= 1) { tprintf("Making config %d of %s permanent\n", cfg, getDict().getUnicharset().debug_str( ambig_class_id).string()); } MakePermanent(AdaptedTemplates, ambig_class_id, cfg, Blob); } } } }
void tesseract::Classify::WriteAdaptedTemplates | ( | FILE * | File, |
ADAPT_TEMPLATES | Templates | ||
) |
This routine saves Templates to File in a binary format.
File | open text file to write Templates to |
Templates | set of adapted templates to write to File |
Definition at line 507 of file adaptive.cpp.
{ int i; /* first write the high level adaptive template struct */ fwrite ((char *) Templates, sizeof (ADAPT_TEMPLATES_STRUCT), 1, File); /* then write out the basic integer templates */ WriteIntTemplates (File, Templates->Templates, unicharset); /* then write out the adaptive info for each class */ for (i = 0; i < (Templates->Templates)->NumClasses; i++) { WriteAdaptedClass (File, Templates->Class[i], Templates->Templates->Class[i]->NumConfigs); } } /* WriteAdaptedTemplates */
void tesseract::Classify::WriteIntTemplates | ( | FILE * | File, |
INT_TEMPLATES | Templates, | ||
const UNICHARSET & | target_unicharset | ||
) |
Definition at line 1172 of file intproto.cpp.
{ /* ** Parameters: ** File open file to write templates to ** Templates templates to save into File ** Globals: none ** Operation: This routine writes Templates to File. The format ** is an efficient binary format. File must already be open ** for writing. ** Return: none ** Exceptions: none ** History: Wed Feb 27 11:48:46 1991, DSJ, Created. */ int i, j; INT_CLASS Class; int unicharset_size = target_unicharset.size(); int version_id = -5; // When negated by the reader -1 becomes +1 etc. if (Templates->NumClasses != unicharset_size) { cprintf("Warning: executing WriteIntTemplates() with %d classes in" " Templates, while target_unicharset size is %d\n", Templates->NumClasses, unicharset_size); } /* first write the high level template struct */ fwrite(&unicharset_size, sizeof(unicharset_size), 1, File); fwrite(&version_id, sizeof(version_id), 1, File); fwrite(&Templates->NumClassPruners, sizeof(Templates->NumClassPruners), 1, File); fwrite(&Templates->NumClasses, sizeof(Templates->NumClasses), 1, File); /* then write out the class pruners */ for (i = 0; i < Templates->NumClassPruners; i++) fwrite(Templates->ClassPruners[i], sizeof(CLASS_PRUNER_STRUCT), 1, File); /* then write out each class */ for (i = 0; i < Templates->NumClasses; i++) { Class = Templates->Class[i]; /* first write out the high level struct for the class */ fwrite(&Class->NumProtos, sizeof(Class->NumProtos), 1, File); fwrite(&Class->NumProtoSets, sizeof(Class->NumProtoSets), 1, File); ASSERT_HOST(Class->NumConfigs == this->fontset_table_.get(Class->font_set_id).size); fwrite(&Class->NumConfigs, sizeof(Class->NumConfigs), 1, File); for (j = 0; j < Class->NumConfigs; ++j) { fwrite(&Class->ConfigLengths[j], sizeof(uinT16), 1, File); } /* then write out the proto lengths */ if (MaxNumIntProtosIn (Class) > 0) { fwrite ((char *) (Class->ProtoLengths), sizeof (uinT8), MaxNumIntProtosIn (Class), File); } /* then write out the proto sets */ for (j = 0; j < Class->NumProtoSets; j++) fwrite ((char *) Class->ProtoSets[j], sizeof (PROTO_SET_STRUCT), 1, File); /* then write the fonts info */ fwrite(&Class->font_set_id, sizeof(int), 1, File); } /* Write the fonts info tables */ this->fontinfo_table_.write(File, NewPermanentTessCallback(write_info)); this->fontinfo_table_.write(File, NewPermanentTessCallback(write_spacing_info)); this->fontset_table_.write(File, NewPermanentTessCallback(write_set)); } /* WriteIntTemplates */
Definition at line 456 of file classify.h.
Definition at line 461 of file classify.h.
Definition at line 460 of file classify.h.
Definition at line 459 of file classify.h.
double tesseract::Classify::certainty_scale = 20.0 |
"Certainty scaling factor"
Definition at line 420 of file classify.h.
"Threshold for good features during adaptive 0-255"
Definition at line 430 of file classify.h.
"Threshold for good protos during adaptive 0-255"
Definition at line 428 of file classify.h.
double tesseract::Classify::classify_adapted_pruning_factor = 2.5 |
"Prune poor adapted results this much worse than best result"
Definition at line 424 of file classify.h.
double tesseract::Classify::classify_adapted_pruning_threshold = -1.0 |
"Threshold at which classify_adapted_pruning_factor starts"
Definition at line 426 of file classify.h.
"Assume the input is numbers [0-9]."
Definition at line 479 of file classify.h.
double tesseract::Classify::classify_char_norm_range = 0.2 |
"Character Normalization Range ..."
Definition at line 379 of file classify.h.
"Exclude fragments that do not match any whole character" " with at least this certainty"
Definition at line 436 of file classify.h.
"Class Pruner Multiplier 0-255: "
Definition at line 448 of file classify.h.
"Class Pruner Threshold 0-255"
Definition at line 446 of file classify.h.
"Class Pruner CutoffStrength: "
Definition at line 450 of file classify.h.
"Bring up graphical debugging windows for fragments training"
Definition at line 438 of file classify.h.
"Classify debug level"
Definition at line 373 of file classify.h.
"Enable match debugger"
Definition at line 397 of file classify.h.
"Enable adaptive classifier"
Definition at line 392 of file classify.h.
bool tesseract::Classify::classify_enable_learning = true |
"Enable adaptive classifier"
Definition at line 372 of file classify.h.
"Integer Matcher Multiplier 0-255: "
Definition at line 452 of file classify.h.
"Class str to debug learning"
Definition at line 442 of file classify.h.
"Learning Debug Level: "
Definition at line 402 of file classify.h.
double tesseract::Classify::classify_max_certainty_margin = 5.5 |
"Veto difference between classifier certainties"
Definition at line 387 of file classify.h.
double tesseract::Classify::classify_max_norm_scale_x = 0.325 |
"Max char x-norm scale ..."
Definition at line 381 of file classify.h.
double tesseract::Classify::classify_max_norm_scale_y = 0.325 |
"Max char y-norm scale ..."
Definition at line 383 of file classify.h.
double tesseract::Classify::classify_max_rating_ratio = 1.5 |
"Veto ratio between classifier ratings"
Definition at line 385 of file classify.h.
double tesseract::Classify::classify_min_norm_scale_x = 0.0 |
"Min char x-norm scale ..."
Definition at line 380 of file classify.h.
double tesseract::Classify::classify_min_norm_scale_y = 0.0 |
"Min char y-norm scale ..."
Definition at line 382 of file classify.h.
double tesseract::Classify::classify_misfit_junk_penalty = 0.0 |
"Penalty to apply when a non-alnum is vertically out of " "its expected textline position"
Definition at line 418 of file classify.h.
"Non-linear stroke-density normalization"
Definition at line 399 of file classify.h.
"Normalization Method ..."
Definition at line 377 of file classify.h.
"Save adapted templates to a file"
Definition at line 396 of file classify.h.
"Use pre-adapted classifier templates"
Definition at line 394 of file classify.h.
bool tesseract::Classify::disable_character_fragments = TRUE |
"Do not include character fragments in the" " results of the classifier"
Definition at line 433 of file classify.h.
Definition at line 463 of file classify.h.
Definition at line 486 of file classify.h.
Definition at line 467 of file classify.h.
Definition at line 475 of file classify.h.
"Dont adapt to i/I at beginning of word"
Definition at line 477 of file classify.h.
IntegerMatcher tesseract::Classify::im_ [protected] |
Definition at line 482 of file classify.h.
double tesseract::Classify::matcher_avg_noise_size = 12.0 |
"Avg. noise blob length: "
Definition at line 408 of file classify.h.
double tesseract::Classify::matcher_bad_match_pad = 0.15 |
"Bad Match Pad (0-1)"
Definition at line 406 of file classify.h.
double tesseract::Classify::matcher_clustering_max_angle_delta = 0.015 |
"Maximum angle delta for prototype clustering"
Definition at line 415 of file classify.h.
"Matcher Debug Flags"
Definition at line 401 of file classify.h.
"Matcher Debug Level"
Definition at line 400 of file classify.h.
bool tesseract::Classify::matcher_debug_separate_windows = FALSE |
"Use two different windows for debugging the matching: " "One for the protos and one for the features."
Definition at line 441 of file classify.h.
double tesseract::Classify::matcher_good_threshold = 0.125 |
"Good Match (0-1)"
Definition at line 403 of file classify.h.
double tesseract::Classify::matcher_great_threshold = 0.0 |
"Great Match (0-1)"
Definition at line 404 of file classify.h.
"Reliable Config Threshold"
Definition at line 411 of file classify.h.
double tesseract::Classify::matcher_perfect_threshold = 0.02 |
"Perfect Match (0-1)"
Definition at line 405 of file classify.h.
"Min # of permanent classes"
Definition at line 409 of file classify.h.
double tesseract::Classify::matcher_rating_margin = 0.1 |
"New template margin (0-1)"
Definition at line 407 of file classify.h.
"Enable adaption even if the ambiguities have not been seen"
Definition at line 413 of file classify.h.
Definition at line 465 of file classify.h.
Definition at line 452 of file classify.h.
bool tesseract::Classify::prioritize_division = FALSE |
"Prioritize blob division over chopping"
Definition at line 370 of file classify.h.
double tesseract::Classify::rating_scale = 1.5 |
"Rating scaling factor"
Definition at line 419 of file classify.h.
ShapeTable* tesseract::Classify::shape_table_ [protected] |
Definition at line 491 of file classify.h.
double tesseract::Classify::speckle_large_max_size = 0.30 |
"Max large speckle size"
Definition at line 480 of file classify.h.
double tesseract::Classify::speckle_rating_penalty = 10.0 |
"Penalty to add to worst rating for noise"
Definition at line 482 of file classify.h.
Definition at line 462 of file classify.h.
"Baseline Normalized Matching"
Definition at line 391 of file classify.h.
"Character Normalized Matching"
Definition at line 390 of file classify.h.
double tesseract::Classify::tessedit_class_miss_scale = 0.00390625 |
"Scale factor for features not used"
Definition at line 422 of file classify.h.
int tesseract::Classify::tessedit_single_match = FALSE |
"Top choice only from CP"
Definition at line 371 of file classify.h.