tesseract
3.03
|
#include <lm_pain_points.h>
Public Member Functions | |
LMPainPoints (int max, float rat, bool fp, const Dict *d, int deb) | |
~LMPainPoints () | |
bool | HasPainPoints (LMPainPointsType pp_type) const |
LMPainPointsType | Deque (MATRIX_COORD *pp, float *priority) |
void | Clear () |
void | GenerateInitial (WERD_RES *word_res) |
void | GenerateFromPath (float rating_cert_scale, ViterbiStateEntry *vse, WERD_RES *word_res) |
void | GenerateFromAmbigs (const DANGERR &fixpt, ViterbiStateEntry *vse, WERD_RES *word_res) |
bool | GenerateForBlamer (double max_char_wh_ratio, WERD_RES *word_res, int col, int row) |
bool | GeneratePainPoint (int col, int row, LMPainPointsType pp_type, float special_priority, bool ok_to_extend, float max_char_wh_ratio, WERD_RES *word_res) |
void | RemapForSplit (int index) |
Static Public Member Functions | |
static const char * | PainPointDescription (LMPainPointsType type) |
Static Public Attributes | |
static const float | kDefaultPainPointPriorityAdjustment = 2.0f |
static const float | kLooseMaxCharWhRatio = 2.5f |
Definition at line 53 of file lm_pain_points.h.
tesseract::LMPainPoints::LMPainPoints | ( | int | max, |
float | rat, | ||
bool | fp, | ||
const Dict * | d, | ||
int | deb | ||
) | [inline] |
Definition at line 66 of file lm_pain_points.h.
: max_heap_size_(max), max_char_wh_ratio_(rat), fixed_pitch_(fp), dict_(d), debug_level_(deb) {}
tesseract::LMPainPoints::~LMPainPoints | ( | ) | [inline] |
Definition at line 69 of file lm_pain_points.h.
{}
void tesseract::LMPainPoints::Clear | ( | ) | [inline] |
Definition at line 82 of file lm_pain_points.h.
{ for (int h = 0; h < LM_PPTYPE_NUM; ++h) pain_points_heaps_[h].clear(); }
LMPainPointsType tesseract::LMPainPoints::Deque | ( | MATRIX_COORD * | pp, |
float * | priority | ||
) |
Definition at line 37 of file lm_pain_points.cpp.
{ for (int h = 0; h < LM_PPTYPE_NUM; ++h) { if (pain_points_heaps_[h].empty()) continue; *priority = pain_points_heaps_[h].PeekTop().key; *pp = pain_points_heaps_[h].PeekTop().data; pain_points_heaps_[h].Pop(NULL); return static_cast<LMPainPointsType>(h); } return LM_PPTYPE_NUM; }
bool tesseract::LMPainPoints::GenerateForBlamer | ( | double | max_char_wh_ratio, |
WERD_RES * | word_res, | ||
int | col, | ||
int | row | ||
) | [inline] |
Definition at line 99 of file lm_pain_points.h.
{ return GeneratePainPoint(col, row, LM_PPTYPE_BLAMER, 0.0, false, max_char_wh_ratio, word_res); }
void tesseract::LMPainPoints::GenerateFromAmbigs | ( | const DANGERR & | fixpt, |
ViterbiStateEntry * | vse, | ||
WERD_RES * | word_res | ||
) |
Definition at line 130 of file lm_pain_points.cpp.
{ // Begins and ends in DANGERR vector now record the blob indices as used // by the ratings matrix. for (int d = 0; d < fixpt.size(); ++d) { const DANGERR_INFO &danger = fixpt[d]; // Only use dangerous ambiguities. if (danger.dangerous) { GeneratePainPoint(danger.begin, danger.end - 1, LM_PPTYPE_AMBIG, vse->cost, true, kLooseMaxCharWhRatio, word_res); } } }
void tesseract::LMPainPoints::GenerateFromPath | ( | float | rating_cert_scale, |
ViterbiStateEntry * | vse, | ||
WERD_RES * | word_res | ||
) |
Definition at line 68 of file lm_pain_points.cpp.
{ ViterbiStateEntry *curr_vse = vse; BLOB_CHOICE *curr_b = vse->curr_b; // The following pain point generation and priority calculation approaches // prioritize exploring paths with low average rating of the known part of // the path, while not relying on the ratings of the pieces to be combined. // // A pain point to combine the neighbors is generated for each pair of // neighboring blobs on the path (the path is represented by vse argument // given to GenerateFromPath()). The priority of each pain point is set to // the average rating (per outline length) of the path, not including the // ratings of the blobs to be combined. // The ratings of the blobs to be combined are not used to calculate the // priority, since it is not possible to determine from their magnitude // whether it will be beneficial to combine the blobs. The reason is that // chopped junk blobs (/ | - ') can have very good (low) ratings, however // combining them will be beneficial. Blobs with high ratings might be // over-joined pieces of characters, but also could be blobs from an unseen // font or chopped pieces of complex characters. while (curr_vse->parent_vse != NULL) { ViterbiStateEntry* parent_vse = curr_vse->parent_vse; const MATRIX_COORD& curr_cell = curr_b->matrix_cell(); const MATRIX_COORD& parent_cell = parent_vse->curr_b->matrix_cell(); MATRIX_COORD pain_coord(parent_cell.col, curr_cell.row); if (!pain_coord.Valid(*word_res->ratings) || !word_res->ratings->Classified(parent_cell.col, curr_cell.row, dict_->WildcardID())) { // rat_subtr contains ratings sum of the two adjacent blobs to be merged. // rat_subtr will be subtracted from the ratings sum of the path, since // the blobs will be joined into a new blob, whose rating is yet unknown. float rat_subtr = curr_b->rating() + parent_vse->curr_b->rating(); // ol_subtr contains the outline length of the blobs that will be joined. float ol_subtr = AssociateUtils::ComputeOutlineLength(rating_cert_scale, *curr_b) + AssociateUtils::ComputeOutlineLength(rating_cert_scale, *(parent_vse->curr_b)); // ol_dif is the outline of the path without the two blobs to be joined. float ol_dif = vse->outline_length - ol_subtr; // priority is set to the average rating of the path per unit of outline, // not counting the ratings of the pieces to be joined. float priority = ol_dif > 0 ? (vse->ratings_sum-rat_subtr)/ol_dif : 0.0; GeneratePainPoint(pain_coord.col, pain_coord.row, LM_PPTYPE_PATH, priority, true, max_char_wh_ratio_, word_res); } else if (debug_level_ > 3) { tprintf("NO pain point (Classified) for col=%d row=%d type=%s\n", pain_coord.col, pain_coord.row, LMPainPointsTypeName[LM_PPTYPE_PATH]); BLOB_CHOICE_IT b_it(word_res->ratings->get(pain_coord.col, pain_coord.row)); for (b_it.mark_cycle_pt(); !b_it.cycled_list(); b_it.forward()) { BLOB_CHOICE* choice = b_it.data(); choice->print_full(); } } curr_vse = parent_vse; curr_b = curr_vse->curr_b; } }
void tesseract::LMPainPoints::GenerateInitial | ( | WERD_RES * | word_res | ) |
Definition at line 48 of file lm_pain_points.cpp.
{ MATRIX *ratings = word_res->ratings; AssociateStats associate_stats; for (int col = 0; col < ratings->dimension(); ++col) { int row_end = MIN(ratings->dimension(), col + ratings->bandwidth() + 1); for (int row = col + 1; row < row_end; ++row) { MATRIX_COORD coord(col, row); if (coord.Valid(*ratings) && ratings->get(col, row) != NOT_CLASSIFIED) continue; // Add an initial pain point if needed. if (ratings->Classified(col, row - 1, dict_->WildcardID()) || (col + 1 < ratings->dimension() && ratings->Classified(col + 1, row, dict_->WildcardID()))) { GeneratePainPoint(col, row, LM_PPTYPE_SHAPE, 0.0, true, max_char_wh_ratio_, word_res); } } } }
bool tesseract::LMPainPoints::GeneratePainPoint | ( | int | col, |
int | row, | ||
LMPainPointsType | pp_type, | ||
float | special_priority, | ||
bool | ok_to_extend, | ||
float | max_char_wh_ratio, | ||
WERD_RES * | word_res | ||
) |
Definition at line 146 of file lm_pain_points.cpp.
{ MATRIX_COORD coord(col, row); if (coord.Valid(*word_res->ratings) && word_res->ratings->Classified(col, row, dict_->WildcardID())) { return false; } if (debug_level_ > 3) { tprintf("Generating pain point for col=%d row=%d type=%s\n", col, row, LMPainPointsTypeName[pp_type]); } // Compute associate stats. AssociateStats associate_stats; AssociateUtils::ComputeStats(col, row, NULL, 0, fixed_pitch_, max_char_wh_ratio, word_res, debug_level_, &associate_stats); // For fixed-pitch fonts/languages: if the current combined blob overlaps // the next blob on the right and it is ok to extend the blob, try extending // the blob until there is no overlap with the next blob on the right or // until the width-to-height ratio becomes too large. if (ok_to_extend) { while (associate_stats.bad_fixed_pitch_right_gap && row + 1 < word_res->ratings->dimension() && !associate_stats.bad_fixed_pitch_wh_ratio) { AssociateUtils::ComputeStats(col, ++row, NULL, 0, fixed_pitch_, max_char_wh_ratio, word_res, debug_level_, &associate_stats); } } if (associate_stats.bad_shape) { if (debug_level_ > 3) { tprintf("Discarded pain point with a bad shape\n"); } return false; } // Insert the new pain point into pain_points_heap_. if (pain_points_heaps_[pp_type].size() < max_heap_size_) { // Compute pain point priority. float priority; if (pp_type == LM_PPTYPE_PATH) { priority = special_priority; } else { priority = associate_stats.gap_sum; } MatrixCoordPair pain_point(priority, MATRIX_COORD(col, row)); pain_points_heaps_[pp_type].Push(&pain_point); if (debug_level_) { tprintf("Added pain point with priority %g\n", priority); } return true; } else { if (debug_level_) tprintf("Pain points heap is full\n"); return false; } }
bool tesseract::LMPainPoints::HasPainPoints | ( | LMPainPointsType | pp_type | ) | const [inline] |
Definition at line 72 of file lm_pain_points.h.
{ return !pain_points_heaps_[pp_type].empty(); }
static const char* tesseract::LMPainPoints::PainPointDescription | ( | LMPainPointsType | type | ) | [inline, static] |
Definition at line 62 of file lm_pain_points.h.
{
return LMPainPointsTypeName[type];
}
void tesseract::LMPainPoints::RemapForSplit | ( | int | index | ) |
Definition at line 207 of file lm_pain_points.cpp.
{ for (int i = 0; i < LM_PPTYPE_NUM; ++i) { GenericVector<MatrixCoordPair>* heap = pain_points_heaps_[i].heap(); for (int j = 0; j < heap->size(); ++j) (*heap)[j].data.MapForSplit(index); } }
const float tesseract::LMPainPoints::kDefaultPainPointPriorityAdjustment = 2.0f [static] |
Definition at line 56 of file lm_pain_points.h.
const float tesseract::LMPainPoints::kLooseMaxCharWhRatio = 2.5f [static] |
Definition at line 60 of file lm_pain_points.h.