--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/mupdf-source/thirdparty/tesseract/src/wordrec/lm_pain_points.cpp	Mon Sep 15 11:43:07 2025 +0200
@@ -0,0 +1,212 @@
+///////////////////////////////////////////////////////////////////////
+// File:        pain_points.cpp
+// Description: Functions that utilize the knowledge about the properties
+//              of the paths explored by the segmentation search in order
+//              to "pain points" - the locations in the ratings matrix
+//              which should be classified next.
+// Author:      Rika Antonova
+// Created:     Mon Jun 20 11:26:43 PST 2012
+//
+// (C) Copyright 2012, Google Inc.
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+// http://www.apache.org/licenses/LICENSE-2.0
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+///////////////////////////////////////////////////////////////////////
+
+#include "lm_pain_points.h"
+
+#include "associate.h"
+#include "dict.h"
+#include "genericheap.h"
+#include "lm_state.h"
+#include "matrix.h"
+#include "pageres.h"
+
+#include <algorithm>
+
+namespace tesseract {
+
+const float LMPainPoints::kDefaultPainPointPriorityAdjustment = 2.0f;
+const float LMPainPoints::kLooseMaxCharWhRatio = 2.5f;
+
+LMPainPointsType LMPainPoints::Deque(MATRIX_COORD *pp, float *priority) {
+  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;
+}
+
+void LMPainPoints::GenerateInitial(WERD_RES *word_res) {
+  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);
+      }
+    }
+  }
+}
+
+void LMPainPoints::GenerateFromPath(float rating_cert_scale, ViterbiStateEntry *vse,
+                                    WERD_RES *word_res) {
+  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;
+  }
+}
+
+void LMPainPoints::GenerateFromAmbigs(const DANGERR &fixpt, ViterbiStateEntry *vse,
+                                      WERD_RES *word_res) {
+  // 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);
+    }
+  }
+}
+
+bool 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) {
+  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;
+  }
+}
+
+/**
+ * Adjusts the pain point coordinates to cope with expansion of the ratings
+ * matrix due to a split of the blob with the given index.
+ */
+void LMPainPoints::RemapForSplit(int index) {
+  for (auto &pain_points_heap : pain_points_heaps_) {
+    std::vector<MatrixCoordPair> &heap = pain_points_heap.heap();
+    for (auto &&entry : heap) {
+      entry.data().MapForSplit(index);
+    }
+  }
+}
+
+} //  namespace tesseract