tesseract::LMPainPoints Class Reference

#include <lm_pain_points.h>

Public Member Functions
	LMPainPoints (int max, float rat, bool fp, const Dict *d, int deb)
	~LMPainPoints ()=default
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	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

Detailed Description

Definition at line 55 of file lm_pain_points.h.

Constructor & Destructor Documentation

LMPainPoints()

tesseract::LMPainPoints::LMPainPoints ( int  max, float  rat, bool  fp, const Dict *  d, int  deb  )

inline

Definition at line 67 of file lm_pain_points.h.

      : max_heap_size_(max)
      , max_char_wh_ratio_(rat)
      , fixed_pitch_(fp)
      , dict_(d)
      , debug_level_(deb) {}

~LMPainPoints()

tesseract::LMPainPoints::~LMPainPoints ( )

default

Member Function Documentation

Clear()

void tesseract::LMPainPoints::Clear ( )

inline

Definition at line 86 of file lm_pain_points.h.

               {
    for (auto &pain_points_heap : pain_points_heaps_) {
      pain_points_heap.clear();
    }
  }

Deque()

LMPainPointsType tesseract::LMPainPoints::Deque	(	MATRIX_COORD *	pp,
		float *	priority
	)

Definition at line 39 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(nullptr);
    return static_cast<LMPainPointsType>(h);
  }
  return LM_PPTYPE_NUM;
}

GenerateFromAmbigs()

void tesseract::LMPainPoints::GenerateFromAmbigs	(	const DANGERR &	fixpt,
		ViterbiStateEntry *	vse,
		WERD_RES *	word_res
	)

Definition at line 129 of file lm_pain_points.cpp.

                                                          {
  // Begins and ends in DANGERR vector now record the blob indices as used
  // by the ratings matrix.
  for (auto danger : fixpt) {
    // Only use dangerous ambiguities.
    if (danger.dangerous) {
      GeneratePainPoint(danger.begin, danger.end - 1, LM_PPTYPE_AMBIG, vse->cost, true,
                        kLooseMaxCharWhRatio, word_res);
    }
  }
}

GenerateFromPath()

void tesseract::LMPainPoints::GenerateFromPath	(	float	rating_cert_scale,
		ViterbiStateEntry *	vse,
		WERD_RES *	word_res
	)

Definition at line 72 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 != nullptr) {
    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;
  }
}

GenerateInitial()

void tesseract::LMPainPoints::GenerateInitial

(

WERD_RES *

word_res

)

Definition at line 52 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 = std::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);
      }
    }
  }
}

GeneratePainPoint()

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 142 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, nullptr, 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, nullptr, 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;
  }
}

HasPainPoints()

bool tesseract::LMPainPoints::HasPainPoints ( LMPainPointsType  pp_type ) const

inline

Definition at line 76 of file lm_pain_points.h.

                                                            {
    return !pain_points_heaps_[pp_type].empty();
  }

PainPointDescription()

static const char* tesseract::LMPainPoints::PainPointDescription ( LMPainPointsType  type )

inlinestatic

Definition at line 63 of file lm_pain_points.h.

                                                                 {
    return LMPainPointsTypeName[type];
  }

RemapForSplit()

void tesseract::LMPainPoints::RemapForSplit

(

int

index

)

Adjusts the pain point coordinates to cope with expansion of the ratings matrix due to a split of the blob with the given index.

Definition at line 203 of file lm_pain_points.cpp.

                                          {
  for (auto &pain_points_heap : pain_points_heaps_) {
    std::vector<MatrixCoordPair> &heap = pain_points_heap.heap();
    for (auto entry : heap) {
      entry.data().MapForSplit(index);
    }
  }
}

Member Data Documentation

kDefaultPainPointPriorityAdjustment

const float tesseract::LMPainPoints::kDefaultPainPointPriorityAdjustment = 2.0f

static

Definition at line 57 of file lm_pain_points.h.

kLooseMaxCharWhRatio

const float tesseract::LMPainPoints::kLooseMaxCharWhRatio = 2.5f

static

Definition at line 61 of file lm_pain_points.h.

---

The documentation for this class was generated from the following files:

• src/wordrec/lm_pain_points.h
• src/wordrec/lm_pain_points.cpp