--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/mupdf-source/thirdparty/tesseract/src/wordrec/language_model.h	Mon Sep 15 11:43:07 2025 +0200
@@ -0,0 +1,376 @@
+///////////////////////////////////////////////////////////////////////
+// File:        language_model.h
+// Description: Functions that utilize the knowledge about the properties,
+//              structure and statistics of the language to help segmentation
+//              search.
+// Author:      Daria Antonova
+//
+// (C) Copyright 2009, 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_LANGUAGE_MODEL_H_
+#define TESSERACT_WORDREC_LANGUAGE_MODEL_H_
+
+#include "associate.h"      // for AssociateStats (ptr only), AssociateUtils
+#include "dawg.h"           // for DawgPositionVector
+#include "dict.h"           // for DawgArgs, Dict
+#include "lm_consistency.h" // for LMConsistencyInfo
+#include "lm_state.h"       // for ViterbiStateEntry, LanguageModelFlagsType
+#include "params.h"         // for DoubleParam, double_VAR_H, IntParam, Boo...
+#include "params_model.h"   // for ParamsModel
+#include "ratngs.h"         // for BLOB_CHOICE (ptr only), BLOB_CHOICE_LIST...
+#include "stopper.h"        // for DANGERR
+
+#include <cmath> // for exp
+
+namespace tesseract {
+
+class UNICHARSET;
+class WERD_RES;
+
+struct BlamerBundle;
+
+template <typename T>
+class UnicityTable;
+
+class LMPainPoints;
+struct FontInfo;
+
+// This class that contains the data structures and functions necessary
+// to represent and use the knowledge about the language.
+class LanguageModel {
+public:
+  // Masks for keeping track of top choices that should not be pruned out.
+  static const LanguageModelFlagsType kSmallestRatingFlag = 0x1;
+  static const LanguageModelFlagsType kLowerCaseFlag = 0x2;
+  static const LanguageModelFlagsType kUpperCaseFlag = 0x4;
+  static const LanguageModelFlagsType kDigitFlag = 0x8;
+  static const LanguageModelFlagsType kXhtConsistentFlag = 0x10;
+
+  // Denominator for normalizing per-letter ngram cost when deriving
+  // penalty adjustments.
+  static const float kMaxAvgNgramCost;
+
+  LanguageModel(const UnicityTable<FontInfo> *fontinfo_table, Dict *dict);
+  ~LanguageModel();
+
+  // Fills the given floats array with features extracted from path represented
+  // by the given ViterbiStateEntry. See ccstruct/params_training_featdef.h
+  // for feature information.
+  // Note: the function assumes that features points to an array of size
+  // PTRAIN_NUM_FEATURE_TYPES.
+  static void ExtractFeaturesFromPath(const ViterbiStateEntry &vse, float features[]);
+
+  // Updates data structures that are used for the duration of the segmentation
+  // search on the current word;
+  void InitForWord(const WERD_CHOICE *prev_word, bool fixed_pitch, float max_char_wh_ratio,
+                   float rating_cert_scale);
+
+  // Updates language model state of the given BLOB_CHOICE_LIST (from
+  // the ratings matrix) and its parent. Updates pain_points if new
+  // problematic points are found in the segmentation graph.
+  //
+  // At most language_model_viterbi_list_size are kept in each
+  // LanguageModelState.viterbi_state_entries list.
+  // At most language_model_viterbi_list_max_num_prunable of those are prunable
+  // (non-dictionary) paths.
+  // The entries that represent dictionary word paths are kept at the front
+  // of the list.
+  // The list ordered by cost that is computed collectively by several
+  // language model components (currently dawg and ngram components).
+  bool UpdateState(bool just_classified, int curr_col, int curr_row, BLOB_CHOICE_LIST *curr_list,
+                   LanguageModelState *parent_node, LMPainPoints *pain_points, WERD_RES *word_res,
+                   BestChoiceBundle *best_choice_bundle, BlamerBundle *blamer_bundle);
+
+  // Returns true if an acceptable best choice was discovered.
+  inline bool AcceptableChoiceFound() {
+    return acceptable_choice_found_;
+  }
+  inline void SetAcceptableChoiceFound(bool val) {
+    acceptable_choice_found_ = val;
+  }
+  // Returns the reference to ParamsModel.
+  inline ParamsModel &getParamsModel() {
+    return params_model_;
+  }
+
+protected:
+  inline float CertaintyScore(float cert) {
+    if (language_model_use_sigmoidal_certainty) {
+      // cert is assumed to be between 0 and -dict_->certainty_scale.
+      // If you enable language_model_use_sigmoidal_certainty, you
+      // need to adjust language_model_ngram_nonmatch_score as well.
+      cert = -cert / dict_->certainty_scale;
+      return 1.0f / (1.0f + exp(10.0f * cert));
+    } else {
+      return (-1.0f / cert);
+    }
+  }
+
+  inline float ComputeAdjustment(int num_problems, float penalty) {
+    if (num_problems == 0) {
+      return 0.0f;
+    }
+    if (num_problems == 1) {
+      return penalty;
+    }
+    return (penalty + (language_model_penalty_increment * static_cast<float>(num_problems - 1)));
+  }
+
+  // Computes the adjustment to the ratings sum based on the given
+  // consistency_info. The paths with invalid punctuation, inconsistent
+  // case and character type are penalized proportionally to the number
+  // of inconsistencies on the path.
+  inline float ComputeConsistencyAdjustment(const LanguageModelDawgInfo *dawg_info,
+                                            const LMConsistencyInfo &consistency_info) {
+    if (dawg_info != nullptr) {
+      return ComputeAdjustment(consistency_info.NumInconsistentCase(),
+                               language_model_penalty_case) +
+             (consistency_info.inconsistent_script ? language_model_penalty_script : 0.0f);
+    }
+    return (ComputeAdjustment(consistency_info.NumInconsistentPunc(), language_model_penalty_punc) +
+            ComputeAdjustment(consistency_info.NumInconsistentCase(), language_model_penalty_case) +
+            ComputeAdjustment(consistency_info.NumInconsistentChartype(),
+                              language_model_penalty_chartype) +
+            ComputeAdjustment(consistency_info.NumInconsistentSpaces(),
+                              language_model_penalty_spacing) +
+            (consistency_info.inconsistent_script ? language_model_penalty_script : 0.0f) +
+            (consistency_info.inconsistent_font ? language_model_penalty_font : 0.0f));
+  }
+
+  // Returns an adjusted ratings sum that includes inconsistency penalties,
+  // penalties for non-dictionary paths and paths with dips in ngram
+  // probability.
+  float ComputeAdjustedPathCost(ViterbiStateEntry *vse);
+
+  // Finds the first lower and upper case letter and first digit in curr_list.
+  // Uses the first character in the list in place of empty results.
+  // Returns true if both alpha and digits are found.
+  bool GetTopLowerUpperDigit(BLOB_CHOICE_LIST *curr_list, BLOB_CHOICE **first_lower,
+                             BLOB_CHOICE **first_upper, BLOB_CHOICE **first_digit) const;
+  // Forces there to be at least one entry in the overall set of the
+  // viterbi_state_entries of each element of parent_node that has the
+  // top_choice_flag set for lower, upper and digit using the same rules as
+  // GetTopLowerUpperDigit, setting the flag on the first found suitable
+  // candidate, whether or not the flag is set on some other parent.
+  // Returns 1 if both alpha and digits are found among the parents, -1 if no
+  // parents are found at all (a legitimate case), and 0 otherwise.
+  int SetTopParentLowerUpperDigit(LanguageModelState *parent_node) const;
+
+  // Finds the next ViterbiStateEntry with which the given unichar_id can
+  // combine sensibly, taking into account any mixed alnum/mixed case
+  // situation, and whether this combination has been inspected before.
+  ViterbiStateEntry *GetNextParentVSE(bool just_classified, bool mixed_alnum, const BLOB_CHOICE *bc,
+                                      LanguageModelFlagsType blob_choice_flags,
+                                      const UNICHARSET &unicharset, WERD_RES *word_res,
+                                      ViterbiStateEntry_IT *vse_it,
+                                      LanguageModelFlagsType *top_choice_flags) const;
+  // Helper function that computes the cost of the path composed of the
+  // path in the given parent ViterbiStateEntry and the given BLOB_CHOICE.
+  // If the new path looks good enough, adds a new ViterbiStateEntry to the
+  // list of viterbi entries in the given BLOB_CHOICE and returns true.
+  bool AddViterbiStateEntry(LanguageModelFlagsType top_choice_flags, float denom, bool word_end,
+                            int curr_col, int curr_row, BLOB_CHOICE *b,
+                            LanguageModelState *curr_state, ViterbiStateEntry *parent_vse,
+                            LMPainPoints *pain_points, WERD_RES *word_res,
+                            BestChoiceBundle *best_choice_bundle, BlamerBundle *blamer_bundle);
+
+  // Determines whether a potential entry is a true top choice and
+  // updates changed accordingly.
+  //
+  // Note: The function assumes that b, top_choice_flags and changed
+  // are not nullptr.
+  void GenerateTopChoiceInfo(ViterbiStateEntry *new_vse, const ViterbiStateEntry *parent_vse,
+                             LanguageModelState *lms);
+
+  // Calls dict_->LetterIsOk() with DawgArgs initialized from parent_vse and
+  // unichar from b.unichar_id(). Constructs and returns LanguageModelDawgInfo
+  // with updated active dawgs, constraints and permuter.
+  //
+  // Note: the caller is responsible for deleting the returned pointer.
+  LanguageModelDawgInfo *GenerateDawgInfo(bool word_end, int curr_col, int curr_row,
+                                          const BLOB_CHOICE &b,
+                                          const ViterbiStateEntry *parent_vse);
+
+  // Computes p(unichar | parent context) and records it in ngram_cost.
+  // If b.unichar_id() is an unlikely continuation of the parent context
+  // sets found_small_prob to true and returns nullptr.
+  // Otherwise creates a new LanguageModelNgramInfo entry containing the
+  // updated context (that includes b.unichar_id() at the end) and returns it.
+  //
+  // Note: the caller is responsible for deleting the returned pointer.
+  LanguageModelNgramInfo *GenerateNgramInfo(const char *unichar, float certainty, float denom,
+                                            int curr_col, int curr_row, float outline_length,
+                                            const ViterbiStateEntry *parent_vse);
+
+  // Computes -(log(prob(classifier)) + log(prob(ngram model)))
+  // for the given unichar in the given context. If there are multiple
+  // unichars at one position - takes the average of their probabilities.
+  // UNICHAR::utf8_step() is used to separate out individual UTF8 characters,
+  // since probability_in_context() can only handle one at a time (while
+  // unicharset might contain ngrams and glyphs composed from multiple UTF8
+  // characters).
+  float ComputeNgramCost(const char *unichar, float certainty, float denom, const char *context,
+                         int *unichar_step_len, bool *found_small_prob, float *ngram_prob);
+
+  // Computes the normalization factors for the classifier confidences
+  // (used by ComputeNgramCost()).
+  float ComputeDenom(BLOB_CHOICE_LIST *curr_list);
+
+  // Fills the given consistency_info based on parent_vse.consistency_info
+  // and on the consistency of the given unichar_id with parent_vse.
+  void FillConsistencyInfo(int curr_col, bool word_end, BLOB_CHOICE *b,
+                           ViterbiStateEntry *parent_vse, WERD_RES *word_res,
+                           LMConsistencyInfo *consistency_info);
+
+  // Constructs WERD_CHOICE by recording unichar_ids of the BLOB_CHOICEs
+  // on the path represented by the given BLOB_CHOICE and language model
+  // state entries (lmse, dse). The path is re-constructed by following
+  // the parent pointers in the lang model state entries). If the
+  // constructed WERD_CHOICE is better than the best/raw choice recorded
+  // in the best_choice_bundle, this function updates the corresponding
+  // fields and sets best_choice_bunldle->updated to true.
+  void UpdateBestChoice(ViterbiStateEntry *vse, LMPainPoints *pain_points, WERD_RES *word_res,
+                        BestChoiceBundle *best_choice_bundle, BlamerBundle *blamer_bundle);
+
+  // Constructs a WERD_CHOICE by tracing parent pointers starting with
+  // the given LanguageModelStateEntry. Returns the constructed word.
+  // Updates best_char_choices, certainties and state if they are not
+  // nullptr (best_char_choices and certainties are assumed to have the
+  // length equal to lmse->length).
+  // The caller is responsible for freeing memory associated with the
+  // returned WERD_CHOICE.
+  WERD_CHOICE *ConstructWord(ViterbiStateEntry *vse, WERD_RES *word_res, DANGERR *fixpt,
+                             BlamerBundle *blamer_bundle, bool *truth_path);
+
+  // Wrapper around AssociateUtils::ComputeStats().
+  inline void ComputeAssociateStats(int col, int row, float max_char_wh_ratio,
+                                    ViterbiStateEntry *parent_vse, WERD_RES *word_res,
+                                    AssociateStats *associate_stats) {
+    AssociateUtils::ComputeStats(
+        col, row, (parent_vse != nullptr) ? &(parent_vse->associate_stats) : nullptr,
+        (parent_vse != nullptr) ? parent_vse->length : 0, fixed_pitch_, max_char_wh_ratio, word_res,
+        language_model_debug_level > 2, associate_stats);
+  }
+
+  // Returns true if the path with such top_choice_flags and dawg_info
+  // could be pruned out (i.e. is neither a system/user/frequent dictionary
+  // nor a top choice path).
+  // In non-space delimited languages all paths can be "somewhat" dictionary
+  // words. In such languages we cannot do dictionary-driven path pruning,
+  // so paths with non-empty dawg_info are considered prunable.
+  inline bool PrunablePath(const ViterbiStateEntry &vse) {
+    if (vse.top_choice_flags) {
+      return false;
+    }
+    if (vse.dawg_info != nullptr &&
+        (vse.dawg_info->permuter == SYSTEM_DAWG_PERM || vse.dawg_info->permuter == USER_DAWG_PERM ||
+         vse.dawg_info->permuter == FREQ_DAWG_PERM)) {
+      return false;
+    }
+    return true;
+  }
+
+  // Returns true if the given ViterbiStateEntry represents an acceptable path.
+  inline bool AcceptablePath(const ViterbiStateEntry &vse) {
+    return (vse.dawg_info != nullptr || vse.Consistent() ||
+            (vse.ngram_info != nullptr && !vse.ngram_info->pruned));
+  }
+
+public:
+  // Parameters.
+  INT_VAR_H(language_model_debug_level);
+  BOOL_VAR_H(language_model_ngram_on);
+  INT_VAR_H(language_model_ngram_order);
+  INT_VAR_H(language_model_viterbi_list_max_num_prunable);
+  INT_VAR_H(language_model_viterbi_list_max_size);
+  double_VAR_H(language_model_ngram_small_prob);
+  double_VAR_H(language_model_ngram_nonmatch_score);
+  BOOL_VAR_H(language_model_ngram_use_only_first_uft8_step);
+  double_VAR_H(language_model_ngram_scale_factor);
+  double_VAR_H(language_model_ngram_rating_factor);
+  BOOL_VAR_H(language_model_ngram_space_delimited_language);
+  INT_VAR_H(language_model_min_compound_length);
+  // Penalties used for adjusting path costs and final word rating.
+  double_VAR_H(language_model_penalty_non_freq_dict_word);
+  double_VAR_H(language_model_penalty_non_dict_word);
+  double_VAR_H(language_model_penalty_punc);
+  double_VAR_H(language_model_penalty_case);
+  double_VAR_H(language_model_penalty_script);
+  double_VAR_H(language_model_penalty_chartype);
+  double_VAR_H(language_model_penalty_font);
+  double_VAR_H(language_model_penalty_spacing);
+  double_VAR_H(language_model_penalty_increment);
+  INT_VAR_H(wordrec_display_segmentations);
+  BOOL_VAR_H(language_model_use_sigmoidal_certainty);
+
+protected:
+  // Member Variables.
+
+  // Temporary DawgArgs struct that is re-used across different words to
+  // avoid dynamic memory re-allocation (should be cleared before each use).
+  DawgArgs dawg_args_;
+  // Scaling for recovering blob outline length from rating and certainty.
+  float rating_cert_scale_ = 0.0f;
+
+  // The following variables are set at construction time.
+
+  // Pointer to fontinfo table (not owned by LanguageModel).
+  const UnicityTable<FontInfo> *fontinfo_table_ = nullptr;
+
+  // Pointer to Dict class, that is used for querying the dictionaries
+  // (the pointer is not owned by LanguageModel).
+  Dict *dict_ = nullptr;
+
+  // TODO(daria): the following variables should become LanguageModel params
+  // when the old code in bestfirst.cpp and heuristic.cpp is deprecated.
+  //
+  // Set to true if we are dealing with fixed pitch text
+  // (set to assume_fixed_pitch_char_segment).
+  bool fixed_pitch_ = false;
+  // Max char width-to-height ratio allowed
+  // (set to segsearch_max_char_wh_ratio).
+  float max_char_wh_ratio_ = 0.0f;
+
+  // The following variables are initialized with InitForWord().
+
+  // String representation of the classification of the previous word
+  // (since this is only used by the character ngram model component,
+  // only the last language_model_ngram_order of the word are stored).
+  std::string prev_word_str_;
+  int prev_word_unichar_step_len_ = 0;
+  // Active dawg vector.
+  DawgPositionVector very_beginning_active_dawgs_; // includes continuation
+  DawgPositionVector beginning_active_dawgs_;
+  // Set to true if acceptable choice was discovered.
+  // Note: it would be nice to use this to terminate the search once an
+  // acceptable choices is found. However we do not do that and once an
+  // acceptable choice is found we finish looking for alternative choices
+  // in the current segmentation graph and then exit the search (no more
+  // classifications are done after an acceptable choice is found).
+  // This is needed in order to let the search find the words very close to
+  // the best choice in rating (e.g. what/What, Cat/cat, etc) and log these
+  // choices. This way the stopper will know that the best choice is not
+  // ambiguous (i.e. there are best choices in the best choice list that have
+  // ratings close to the very best one) and will be less likely to mis-adapt.
+  bool acceptable_choice_found_ = false;
+  // Set to true if a choice representing correct segmentation was explored.
+  bool correct_segmentation_explored_ = false;
+
+  // Params models containing weights for computing ViterbiStateEntry costs.
+  ParamsModel params_model_;
+};
+
+} // namespace tesseract
+
+#endif // TESSERACT_WORDREC_LANGUAGE_MODEL_H_