--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/mupdf-source/thirdparty/tesseract/src/wordrec/wordrec.h	Mon Sep 15 11:43:07 2025 +0200
@@ -0,0 +1,452 @@
+///////////////////////////////////////////////////////////////////////
+// File:        wordrec.h
+// Description: wordrec class.
+// Author:      Samuel Charron
+//
+// (C) Copyright 2006, 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.
+//
+///////////////////////////////////////////////////////////////////////
+
+#ifndef TESSERACT_WORDREC_WORDREC_H_
+#define TESSERACT_WORDREC_WORDREC_H_
+
+#ifdef HAVE_CONFIG_H
+#  include "config_auto.h" // DISABLED_LEGACY_ENGINE
+#endif
+
+#ifdef DISABLED_LEGACY_ENGINE
+
+#  include <cstdint>    // for int16_t, int32_t
+#  include "classify.h" // for Classify
+#  include "params.h"   // for INT_VAR_H, IntParam, BOOL_VAR_H, BoolP...
+#  include "ratngs.h"   // for WERD_CHOICE
+
+namespace tesseract {
+class TessdataManager;
+}
+
+namespace tesseract {
+
+/* ccmain/tstruct.cpp */
+
+class TESS_API Wordrec : public Classify {
+public:
+  // config parameters
+
+  BOOL_VAR_H(wordrec_debug_blamer);
+  BOOL_VAR_H(wordrec_run_blamer);
+
+  // methods
+  Wordrec();
+  virtual ~Wordrec() = default;
+
+  // tface.cpp
+  void program_editup(const std::string &textbase, TessdataManager *init_classifier,
+                      TessdataManager *init_dict);
+  void program_editdown(int32_t elapsed_time);
+  int end_recog();
+  int dict_word(const WERD_CHOICE &word);
+
+  // Member variables
+  WERD_CHOICE *prev_word_best_choice_;
+};
+
+} // namespace tesseract
+
+#else // DISABLED_LEGACY_ENGINE not defined
+
+#  include <memory>
+#  include "associate.h"
+#  include "chop.h"     // for PointHeap, MAX_NUM_POINTS
+#  include "classify.h" // for Classify
+#  include "dict.h"
+#  include "elst.h"     // for ELIST_ITERATOR, ELISTIZEH, ELIST_LINK
+#  include "findseam.h" // for SeamQueue, SeamPile
+#  include "language_model.h"
+#  include "matrix.h"
+#  include "oldlist.h" // for LIST
+#  include "params.h"  // for INT_VAR_H, IntParam, BOOL_VAR_H, BoolP...
+#  include "points.h"  // for ICOORD
+#  include "ratngs.h"  // for BLOB_CHOICE_LIST (ptr only), BLOB_CHOI...
+#  include "seam.h"    // for SEAM (ptr only), PRIORITY
+#  include "stopper.h" // for DANGERR
+
+#  include <cstdint> // for int16_t, int32_t
+
+namespace tesseract {
+
+class EDGEPT_CLIST;
+class MATRIX;
+class TBOX;
+class UNICHARSET;
+class WERD_RES;
+
+class LMPainPoints;
+class TessdataManager;
+struct BestChoiceBundle;
+
+struct BlamerBundle;
+struct EDGEPT;
+struct MATRIX_COORD;
+struct SPLIT;
+struct TBLOB;
+struct TESSLINE;
+struct TWERD;
+
+// A class for storing which nodes are to be processed by the segmentation
+// search. There is a single SegSearchPending for each column in the ratings
+// matrix, and it indicates whether the segsearch should combine all
+// BLOB_CHOICES in the column, or just the given row with the parents
+// corresponding to *this SegSearchPending, and whether only updated parent
+// ViterbiStateEntries should be combined, or all, with the BLOB_CHOICEs.
+class SegSearchPending {
+public:
+  SegSearchPending()
+      : classified_row_(-1), revisit_whole_column_(false), column_classified_(false) {}
+
+  // Marks the whole column as just classified. Used to start a search on
+  // a newly initialized ratings matrix.
+  void SetColumnClassified() {
+    column_classified_ = true;
+  }
+  // Marks the matrix entry at the given row as just classified.
+  // Used after classifying a new matrix cell.
+  // Additional to, not overriding a previous RevisitWholeColumn.
+  void SetBlobClassified(int row) {
+    classified_row_ = row;
+  }
+  // Marks the whole column as needing work, but not just classified.
+  // Used when the parent vse list is updated.
+  // Additional to, not overriding a previous SetBlobClassified.
+  void RevisitWholeColumn() {
+    revisit_whole_column_ = true;
+  }
+
+  // Clears *this to indicate no work to do.
+  void Clear() {
+    classified_row_ = -1;
+    revisit_whole_column_ = false;
+    column_classified_ = false;
+  }
+
+  // Returns true if there are updates to do in the column that *this
+  // represents.
+  bool WorkToDo() const {
+    return revisit_whole_column_ || column_classified_ || classified_row_ >= 0;
+  }
+  // Returns true if the given row was just classified.
+  bool IsRowJustClassified(int row) const {
+    return row == classified_row_ || column_classified_;
+  }
+  // Returns the single row to process if there is only one, otherwise -1.
+  int SingleRow() const {
+    return revisit_whole_column_ || column_classified_ ? -1 : classified_row_;
+  }
+
+private:
+  // If non-negative, indicates the single row in the ratings matrix that has
+  // just been classified, and so should be combined with all the parents in the
+  // column that this SegSearchPending represents.
+  // Operates independently of revisit_whole_column.
+  int classified_row_;
+  // If revisit_whole_column is true, then all BLOB_CHOICEs in this column will
+  // be processed, but classified_row can indicate a row that is newly
+  // classified. Overridden if column_classified is true.
+  bool revisit_whole_column_;
+  // If column_classified is true, parent vses are processed with all rows
+  // regardless of whether they are just updated, overriding
+  // revisit_whole_column and classified_row.
+  bool column_classified_;
+};
+
+/* ccmain/tstruct.cpp *********************************************************/
+class FRAGMENT : public ELIST_LINK {
+public:
+  FRAGMENT() { // constructor
+  }
+  FRAGMENT(EDGEPT *head_pt,  // start
+           EDGEPT *tail_pt); // end
+
+  ICOORD head;    // coords of start
+  ICOORD tail;    // coords of end
+  EDGEPT *headpt; // start point
+  EDGEPT *tailpt; // end point
+};
+ELISTIZEH(FRAGMENT)
+
+class TESS_API Wordrec : public Classify {
+public:
+  // config parameters *******************************************************
+  BOOL_VAR_H(merge_fragments_in_matrix);
+  BOOL_VAR_H(wordrec_enable_assoc);
+  BOOL_VAR_H(force_word_assoc);
+  INT_VAR_H(repair_unchopped_blobs);
+  double_VAR_H(tessedit_certainty_threshold);
+  INT_VAR_H(chop_debug);
+  BOOL_VAR_H(chop_enable);
+  BOOL_VAR_H(chop_vertical_creep);
+  INT_VAR_H(chop_split_length);
+  INT_VAR_H(chop_same_distance);
+  INT_VAR_H(chop_min_outline_points);
+  INT_VAR_H(chop_seam_pile_size);
+  BOOL_VAR_H(chop_new_seam_pile);
+  INT_VAR_H(chop_inside_angle);
+  INT_VAR_H(chop_min_outline_area);
+  double_VAR_H(chop_split_dist_knob);
+  double_VAR_H(chop_overlap_knob);
+  double_VAR_H(chop_center_knob);
+  INT_VAR_H(chop_centered_maxwidth);
+  double_VAR_H(chop_sharpness_knob);
+  double_VAR_H(chop_width_change_knob);
+  double_VAR_H(chop_ok_split);
+  double_VAR_H(chop_good_split);
+  INT_VAR_H(chop_x_y_weight);
+  BOOL_VAR_H(assume_fixed_pitch_char_segment);
+  INT_VAR_H(wordrec_debug_level);
+  INT_VAR_H(wordrec_max_join_chunks);
+  BOOL_VAR_H(wordrec_skip_no_truth_words);
+  BOOL_VAR_H(wordrec_debug_blamer);
+  BOOL_VAR_H(wordrec_run_blamer);
+  INT_VAR_H(segsearch_debug_level);
+  INT_VAR_H(segsearch_max_pain_points);
+  INT_VAR_H(segsearch_max_futile_classifications);
+  double_VAR_H(segsearch_max_char_wh_ratio);
+  BOOL_VAR_H(save_alt_choices);
+
+  // methods from wordrec/*.cpp ***********************************************
+  Wordrec();
+  ~Wordrec() override = default;
+
+  // Fills word->alt_choices with alternative paths found during
+  // chopping/segmentation search that are kept in best_choices.
+  void SaveAltChoices(const LIST &best_choices, WERD_RES *word);
+
+  // Fills character choice lattice in the given BlamerBundle
+  // using the given ratings matrix and best choice list.
+  void FillLattice(const MATRIX &ratings, const WERD_CHOICE_LIST &best_choices,
+                   const UNICHARSET &unicharset, BlamerBundle *blamer_bundle);
+
+  // Calls fill_lattice_ member function
+  // (assumes that fill_lattice_ is not nullptr).
+  void CallFillLattice(const MATRIX &ratings, const WERD_CHOICE_LIST &best_choices,
+                       const UNICHARSET &unicharset, BlamerBundle *blamer_bundle) {
+    (this->*fill_lattice_)(ratings, best_choices, unicharset, blamer_bundle);
+  }
+
+  // tface.cpp
+  void program_editup(const std::string &textbase, TessdataManager *init_classifier,
+                      TessdataManager *init_dict);
+  void cc_recog(WERD_RES *word);
+  void program_editdown(int32_t elapsed_time);
+  void set_pass1();
+  void set_pass2();
+  int end_recog();
+  BLOB_CHOICE_LIST *call_matcher(TBLOB *blob);
+  int dict_word(const WERD_CHOICE &word);
+  // wordclass.cpp
+  BLOB_CHOICE_LIST *classify_blob(TBLOB *blob, const char *string, ScrollView::Color color,
+                                  BlamerBundle *blamer_bundle);
+
+  // segsearch.cpp
+  // SegSearch works on the lower diagonal matrix of BLOB_CHOICE_LISTs.
+  // Each entry in the matrix represents the classification choice
+  // for a chunk, i.e. an entry in row 2, column 1 represents the list
+  // of ratings for the chunks 1 and 2 classified as a single blob.
+  // The entries on the diagonal of the matrix are classifier choice lists
+  // for a single chunk from the maximal segmentation.
+  //
+  // The ratings matrix given to SegSearch represents the segmentation
+  // graph / trellis for the current word. The nodes in the graph are the
+  // individual BLOB_CHOICEs in each of the BLOB_CHOICE_LISTs in the ratings
+  // matrix. The children of each node (nodes connected by outgoing links)
+  // are the entries in the column that is equal to node's row+1. The parents
+  // (nodes connected by the incoming links) are the entries in the row that
+  // is equal to the node's column-1. Here is an example ratings matrix:
+  //
+  //    0    1    2   3   4
+  //  -------------------------
+  // 0| c,(                   |
+  // 1| d    l,1              |
+  // 2|           o           |
+  // 3|              c,(      |
+  // 4|              g,y  l,1 |
+  //  -------------------------
+  //
+  // In the example above node "o" has children (outgoing connection to nodes)
+  // "c","(","g","y" and parents (incoming connections from nodes) "l","1","d".
+  //
+  // The objective of the search is to find the least cost path, where the cost
+  // is determined by the language model components and the properties of the
+  // cut between the blobs on the path. SegSearch starts by populating the
+  // matrix with the all the entries that were classified by the chopper and
+  // finding the initial best path. Based on the classifier ratings, language
+  // model scores and the properties of each cut, a list of "pain points" is
+  // constructed - those are the points on the path where the choices do not
+  // look consistent with the neighboring choices, the cuts look particularly
+  // problematic, or the certainties of the blobs are low. The most troublesome
+  // "pain point" is picked from the list and the new entry in the ratings
+  // matrix corresponding to this "pain point" is filled in. Then the language
+  // model state is updated to reflect the new classification and the new
+  // "pain points" are added to the list and the next most troublesome
+  // "pain point" is determined. This continues until either the word choice
+  // composed from the best paths in the segmentation graph is "good enough"
+  // (e.g. above a certain certainty threshold, is an unambiguous dictionary
+  // word, etc) or there are no more "pain points" to explore.
+  //
+  // If associate_blobs is set to false no new classifications will be done
+  // to combine blobs. Segmentation search will run only one "iteration"
+  // on the classifications already recorded in chunks_record.ratings.
+  //
+  // Note: this function assumes that word_res, best_choice_bundle arguments
+  // are not nullptr.
+  void SegSearch(WERD_RES *word_res, BestChoiceBundle *best_choice_bundle,
+                 BlamerBundle *blamer_bundle);
+
+  // Setup and run just the initial segsearch on an established matrix,
+  // without doing any additional chopping or joining.
+  // (Internal factored version that can be used as part of the main SegSearch.)
+  void InitialSegSearch(WERD_RES *word_res, LMPainPoints *pain_points,
+                        std::vector<SegSearchPending> *pending,
+                        BestChoiceBundle *best_choice_bundle, BlamerBundle *blamer_bundle);
+
+  // chop.cpp
+  PRIORITY point_priority(EDGEPT *point);
+  void add_point_to_list(PointHeap *point_heap, EDGEPT *point);
+  // Returns true if the edgept supplied as input is an inside angle.  This
+  // is determined by the angular change of the vectors from point to point.
+  bool is_inside_angle(EDGEPT *pt);
+  int angle_change(EDGEPT *point1, EDGEPT *point2, EDGEPT *point3);
+  EDGEPT *pick_close_point(EDGEPT *critical_point, EDGEPT *vertical_point, int *best_dist);
+  void prioritize_points(TESSLINE *outline, PointHeap *points);
+  void new_min_point(EDGEPT *local_min, PointHeap *points);
+  void new_max_point(EDGEPT *local_max, PointHeap *points);
+  void vertical_projection_point(EDGEPT *split_point, EDGEPT *target_point, EDGEPT **best_point,
+                                 EDGEPT_CLIST *new_points);
+
+  // chopper.cpp
+  SEAM *attempt_blob_chop(TWERD *word, TBLOB *blob, int32_t blob_number, bool italic_blob,
+                          const std::vector<SEAM *> &seams);
+  SEAM *chop_numbered_blob(TWERD *word, int32_t blob_number, bool italic_blob,
+                           const std::vector<SEAM *> &seams);
+  SEAM *chop_overlapping_blob(const std::vector<TBOX> &boxes, bool italic_blob, WERD_RES *word_res,
+                              unsigned *blob_number);
+  SEAM *improve_one_blob(const std::vector<BLOB_CHOICE *> &blob_choices, DANGERR *fixpt,
+                         bool split_next_to_fragment, bool italic_blob, WERD_RES *word,
+                         unsigned *blob_number);
+  SEAM *chop_one_blob(const std::vector<TBOX> &boxes,
+                      const std::vector<BLOB_CHOICE *> &blob_choices, WERD_RES *word_res,
+                      unsigned *blob_number);
+  void chop_word_main(WERD_RES *word);
+  void improve_by_chopping(float rating_cert_scale, WERD_RES *word,
+                           BestChoiceBundle *best_choice_bundle, BlamerBundle *blamer_bundle,
+                           LMPainPoints *pain_points, std::vector<SegSearchPending> *pending);
+  int select_blob_to_split(const std::vector<BLOB_CHOICE *> &blob_choices, float rating_ceiling,
+                           bool split_next_to_fragment);
+  int select_blob_to_split_from_fixpt(DANGERR *fixpt);
+
+  // findseam.cpp
+  void add_seam_to_queue(float new_priority, SEAM *new_seam, SeamQueue *seams);
+  void choose_best_seam(SeamQueue *seam_queue, const SPLIT *split, PRIORITY priority,
+                        SEAM **seam_result, TBLOB *blob, SeamPile *seam_pile);
+  void combine_seam(const SeamPile &seam_pile, const SEAM *seam, SeamQueue *seam_queue);
+  SEAM *pick_good_seam(TBLOB *blob);
+  void try_point_pairs(EDGEPT *points[MAX_NUM_POINTS], int16_t num_points, SeamQueue *seam_queue,
+                       SeamPile *seam_pile, SEAM **seam, TBLOB *blob);
+  void try_vertical_splits(EDGEPT *points[MAX_NUM_POINTS], int16_t num_points,
+                           EDGEPT_CLIST *new_points, SeamQueue *seam_queue, SeamPile *seam_pile,
+                           SEAM **seam, TBLOB *blob);
+
+  // gradechop.cpp
+  PRIORITY grade_split_length(SPLIT *split);
+  PRIORITY grade_sharpness(SPLIT *split);
+
+  // outlines.cpp
+  bool near_point(EDGEPT *point, EDGEPT *line_pt_0, EDGEPT *line_pt_1, EDGEPT **near_pt);
+
+  // pieces.cpp
+  virtual BLOB_CHOICE_LIST *classify_piece(const std::vector<SEAM *> &seams, int16_t start,
+                                           int16_t end, const char *description, TWERD *word,
+                                           BlamerBundle *blamer_bundle);
+
+  // Member variables.
+
+  std::unique_ptr<LanguageModel> language_model_;
+  PRIORITY pass2_ok_split;
+  // Stores the best choice for the previous word in the paragraph.
+  // This variable is modified by PAGE_RES_IT when iterating over
+  // words to OCR on the page.
+  WERD_CHOICE *prev_word_best_choice_;
+
+  // Function used to fill char choice lattices.
+  void (Wordrec::*fill_lattice_)(const MATRIX &ratings, const WERD_CHOICE_LIST &best_choices,
+                                 const UNICHARSET &unicharset, BlamerBundle *blamer_bundle);
+
+protected:
+  inline bool SegSearchDone(int num_futile_classifications) {
+    return (language_model_->AcceptableChoiceFound() ||
+            num_futile_classifications >= segsearch_max_futile_classifications);
+  }
+
+  // Updates the language model state recorded for the child entries specified
+  // in pending[starting_col]. Enqueues the children of the updated entries
+  // into pending and proceeds to update (and remove from pending) all the
+  // remaining entries in pending[col] (col >= starting_col). Upon termination
+  // of this function all the pending[col] lists will be empty.
+  //
+  // The arguments:
+  //
+  // starting_col: index of the column in chunks_record->ratings from
+  // which the update should be started
+  //
+  // pending: list of entries listing chunks_record->ratings entries
+  // that should be updated
+  //
+  // pain_points: priority heap listing the pain points generated by
+  // the language model
+  //
+  // temp_pain_points: temporary storage for tentative pain points generated
+  // by the language model after a single call to LanguageModel::UpdateState()
+  // (the argument is passed in rather than created before each
+  // LanguageModel::UpdateState() call to avoid dynamic memory re-allocation)
+  //
+  // best_choice_bundle: a collection of variables that should be updated
+  // if a new best choice is found
+  //
+  void UpdateSegSearchNodes(float rating_cert_scale, int starting_col,
+                            std::vector<SegSearchPending> *pending, WERD_RES *word_res,
+                            LMPainPoints *pain_points, BestChoiceBundle *best_choice_bundle,
+                            BlamerBundle *blamer_bundle);
+
+  // Process the given pain point: classify the corresponding blob, enqueue
+  // new pain points to join the newly classified blob with its neighbors.
+  void ProcessSegSearchPainPoint(float pain_point_priority, const MATRIX_COORD &pain_point,
+                                 const char *pain_point_type,
+                                 std::vector<SegSearchPending> *pending, WERD_RES *word_res,
+                                 LMPainPoints *pain_points, BlamerBundle *blamer_bundle);
+  // Resets enough of the results so that the Viterbi search is re-run.
+  // Needed when the n-gram model is enabled, as the multi-length comparison
+  // implementation will re-value existing paths to worse values.
+  void ResetNGramSearch(WERD_RES *word_res, BestChoiceBundle *best_choice_bundle,
+                        std::vector<SegSearchPending> &pending);
+
+  // Add pain points for classifying blobs on the correct segmentation path
+  // (so that we can evaluate correct segmentation path and discover the reason
+  // for incorrect result).
+  void InitBlamerForSegSearch(WERD_RES *word_res, LMPainPoints *pain_points,
+                              BlamerBundle *blamer_bundle, std::string &blamer_debug);
+};
+
+} // namespace tesseract
+
+#endif // DISABLED_LEGACY_ENGINE
+
+#endif // TESSERACT_WORDREC_WORDREC_H_