Mercurial > hgrepos > Python2 > PyMuPDF
annotate mupdf-source/thirdparty/tesseract/src/wordrec/language_model.h @ 2:b50eed0cc0ef upstream
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| author | Franz Glasner <fzglas.hg@dom66.de> |
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| date | Mon, 15 Sep 2025 11:43:07 +0200 |
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1 /////////////////////////////////////////////////////////////////////// |
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2 // File: language_model.h |
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3 // Description: Functions that utilize the knowledge about the properties, |
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4 // structure and statistics of the language to help segmentation |
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5 // search. |
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6 // Author: Daria Antonova |
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7 // |
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8 // (C) Copyright 2009, Google Inc. |
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9 // Licensed under the Apache License, Version 2.0 (the "License"); |
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10 // you may not use this file except in compliance with the License. |
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11 // You may obtain a copy of the License at |
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12 // http://www.apache.org/licenses/LICENSE-2.0 |
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13 // Unless required by applicable law or agreed to in writing, software |
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14 // distributed under the License is distributed on an "AS IS" BASIS, |
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15 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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16 // See the License for the specific language governing permissions and |
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17 // limitations under the License. |
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18 // |
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19 /////////////////////////////////////////////////////////////////////// |
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20 |
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21 #ifndef TESSERACT_WORDREC_LANGUAGE_MODEL_H_ |
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22 #define TESSERACT_WORDREC_LANGUAGE_MODEL_H_ |
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23 |
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24 #include "associate.h" // for AssociateStats (ptr only), AssociateUtils |
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25 #include "dawg.h" // for DawgPositionVector |
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26 #include "dict.h" // for DawgArgs, Dict |
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27 #include "lm_consistency.h" // for LMConsistencyInfo |
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28 #include "lm_state.h" // for ViterbiStateEntry, LanguageModelFlagsType |
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29 #include "params.h" // for DoubleParam, double_VAR_H, IntParam, Boo... |
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30 #include "params_model.h" // for ParamsModel |
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31 #include "ratngs.h" // for BLOB_CHOICE (ptr only), BLOB_CHOICE_LIST... |
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32 #include "stopper.h" // for DANGERR |
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33 |
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34 #include <cmath> // for exp |
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35 |
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36 namespace tesseract { |
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37 |
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38 class UNICHARSET; |
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39 class WERD_RES; |
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40 |
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41 struct BlamerBundle; |
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42 |
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43 template <typename T> |
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44 class UnicityTable; |
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45 |
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46 class LMPainPoints; |
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47 struct FontInfo; |
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48 |
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49 // This class that contains the data structures and functions necessary |
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50 // to represent and use the knowledge about the language. |
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51 class LanguageModel { |
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52 public: |
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53 // Masks for keeping track of top choices that should not be pruned out. |
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54 static const LanguageModelFlagsType kSmallestRatingFlag = 0x1; |
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55 static const LanguageModelFlagsType kLowerCaseFlag = 0x2; |
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56 static const LanguageModelFlagsType kUpperCaseFlag = 0x4; |
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57 static const LanguageModelFlagsType kDigitFlag = 0x8; |
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58 static const LanguageModelFlagsType kXhtConsistentFlag = 0x10; |
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59 |
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60 // Denominator for normalizing per-letter ngram cost when deriving |
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61 // penalty adjustments. |
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62 static const float kMaxAvgNgramCost; |
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63 |
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64 LanguageModel(const UnicityTable<FontInfo> *fontinfo_table, Dict *dict); |
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65 ~LanguageModel(); |
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66 |
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67 // Fills the given floats array with features extracted from path represented |
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68 // by the given ViterbiStateEntry. See ccstruct/params_training_featdef.h |
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69 // for feature information. |
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70 // Note: the function assumes that features points to an array of size |
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71 // PTRAIN_NUM_FEATURE_TYPES. |
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72 static void ExtractFeaturesFromPath(const ViterbiStateEntry &vse, float features[]); |
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73 |
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74 // Updates data structures that are used for the duration of the segmentation |
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75 // search on the current word; |
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76 void InitForWord(const WERD_CHOICE *prev_word, bool fixed_pitch, float max_char_wh_ratio, |
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77 float rating_cert_scale); |
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78 |
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79 // Updates language model state of the given BLOB_CHOICE_LIST (from |
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80 // the ratings matrix) and its parent. Updates pain_points if new |
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81 // problematic points are found in the segmentation graph. |
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82 // |
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83 // At most language_model_viterbi_list_size are kept in each |
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84 // LanguageModelState.viterbi_state_entries list. |
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85 // At most language_model_viterbi_list_max_num_prunable of those are prunable |
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86 // (non-dictionary) paths. |
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87 // The entries that represent dictionary word paths are kept at the front |
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88 // of the list. |
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89 // The list ordered by cost that is computed collectively by several |
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90 // language model components (currently dawg and ngram components). |
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91 bool UpdateState(bool just_classified, int curr_col, int curr_row, BLOB_CHOICE_LIST *curr_list, |
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92 LanguageModelState *parent_node, LMPainPoints *pain_points, WERD_RES *word_res, |
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93 BestChoiceBundle *best_choice_bundle, BlamerBundle *blamer_bundle); |
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94 |
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95 // Returns true if an acceptable best choice was discovered. |
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96 inline bool AcceptableChoiceFound() { |
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97 return acceptable_choice_found_; |
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98 } |
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99 inline void SetAcceptableChoiceFound(bool val) { |
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100 acceptable_choice_found_ = val; |
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101 } |
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102 // Returns the reference to ParamsModel. |
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103 inline ParamsModel &getParamsModel() { |
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104 return params_model_; |
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105 } |
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106 |
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107 protected: |
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108 inline float CertaintyScore(float cert) { |
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109 if (language_model_use_sigmoidal_certainty) { |
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110 // cert is assumed to be between 0 and -dict_->certainty_scale. |
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111 // If you enable language_model_use_sigmoidal_certainty, you |
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112 // need to adjust language_model_ngram_nonmatch_score as well. |
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113 cert = -cert / dict_->certainty_scale; |
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114 return 1.0f / (1.0f + exp(10.0f * cert)); |
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115 } else { |
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116 return (-1.0f / cert); |
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117 } |
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118 } |
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119 |
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120 inline float ComputeAdjustment(int num_problems, float penalty) { |
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121 if (num_problems == 0) { |
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122 return 0.0f; |
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123 } |
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124 if (num_problems == 1) { |
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125 return penalty; |
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126 } |
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127 return (penalty + (language_model_penalty_increment * static_cast<float>(num_problems - 1))); |
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128 } |
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129 |
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130 // Computes the adjustment to the ratings sum based on the given |
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131 // consistency_info. The paths with invalid punctuation, inconsistent |
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132 // case and character type are penalized proportionally to the number |
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133 // of inconsistencies on the path. |
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134 inline float ComputeConsistencyAdjustment(const LanguageModelDawgInfo *dawg_info, |
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135 const LMConsistencyInfo &consistency_info) { |
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136 if (dawg_info != nullptr) { |
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137 return ComputeAdjustment(consistency_info.NumInconsistentCase(), |
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138 language_model_penalty_case) + |
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139 (consistency_info.inconsistent_script ? language_model_penalty_script : 0.0f); |
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140 } |
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141 return (ComputeAdjustment(consistency_info.NumInconsistentPunc(), language_model_penalty_punc) + |
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142 ComputeAdjustment(consistency_info.NumInconsistentCase(), language_model_penalty_case) + |
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143 ComputeAdjustment(consistency_info.NumInconsistentChartype(), |
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144 language_model_penalty_chartype) + |
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145 ComputeAdjustment(consistency_info.NumInconsistentSpaces(), |
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146 language_model_penalty_spacing) + |
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147 (consistency_info.inconsistent_script ? language_model_penalty_script : 0.0f) + |
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148 (consistency_info.inconsistent_font ? language_model_penalty_font : 0.0f)); |
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149 } |
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150 |
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151 // Returns an adjusted ratings sum that includes inconsistency penalties, |
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152 // penalties for non-dictionary paths and paths with dips in ngram |
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153 // probability. |
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154 float ComputeAdjustedPathCost(ViterbiStateEntry *vse); |
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155 |
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156 // Finds the first lower and upper case letter and first digit in curr_list. |
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157 // Uses the first character in the list in place of empty results. |
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158 // Returns true if both alpha and digits are found. |
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159 bool GetTopLowerUpperDigit(BLOB_CHOICE_LIST *curr_list, BLOB_CHOICE **first_lower, |
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160 BLOB_CHOICE **first_upper, BLOB_CHOICE **first_digit) const; |
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161 // Forces there to be at least one entry in the overall set of the |
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162 // viterbi_state_entries of each element of parent_node that has the |
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163 // top_choice_flag set for lower, upper and digit using the same rules as |
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164 // GetTopLowerUpperDigit, setting the flag on the first found suitable |
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165 // candidate, whether or not the flag is set on some other parent. |
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166 // Returns 1 if both alpha and digits are found among the parents, -1 if no |
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167 // parents are found at all (a legitimate case), and 0 otherwise. |
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168 int SetTopParentLowerUpperDigit(LanguageModelState *parent_node) const; |
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169 |
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170 // Finds the next ViterbiStateEntry with which the given unichar_id can |
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171 // combine sensibly, taking into account any mixed alnum/mixed case |
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172 // situation, and whether this combination has been inspected before. |
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173 ViterbiStateEntry *GetNextParentVSE(bool just_classified, bool mixed_alnum, const BLOB_CHOICE *bc, |
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174 LanguageModelFlagsType blob_choice_flags, |
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175 const UNICHARSET &unicharset, WERD_RES *word_res, |
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176 ViterbiStateEntry_IT *vse_it, |
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177 LanguageModelFlagsType *top_choice_flags) const; |
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178 // Helper function that computes the cost of the path composed of the |
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179 // path in the given parent ViterbiStateEntry and the given BLOB_CHOICE. |
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180 // If the new path looks good enough, adds a new ViterbiStateEntry to the |
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181 // list of viterbi entries in the given BLOB_CHOICE and returns true. |
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182 bool AddViterbiStateEntry(LanguageModelFlagsType top_choice_flags, float denom, bool word_end, |
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183 int curr_col, int curr_row, BLOB_CHOICE *b, |
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184 LanguageModelState *curr_state, ViterbiStateEntry *parent_vse, |
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185 LMPainPoints *pain_points, WERD_RES *word_res, |
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186 BestChoiceBundle *best_choice_bundle, BlamerBundle *blamer_bundle); |
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187 |
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188 // Determines whether a potential entry is a true top choice and |
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189 // updates changed accordingly. |
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190 // |
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191 // Note: The function assumes that b, top_choice_flags and changed |
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192 // are not nullptr. |
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193 void GenerateTopChoiceInfo(ViterbiStateEntry *new_vse, const ViterbiStateEntry *parent_vse, |
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194 LanguageModelState *lms); |
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195 |
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196 // Calls dict_->LetterIsOk() with DawgArgs initialized from parent_vse and |
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197 // unichar from b.unichar_id(). Constructs and returns LanguageModelDawgInfo |
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198 // with updated active dawgs, constraints and permuter. |
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199 // |
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200 // Note: the caller is responsible for deleting the returned pointer. |
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201 LanguageModelDawgInfo *GenerateDawgInfo(bool word_end, int curr_col, int curr_row, |
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202 const BLOB_CHOICE &b, |
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203 const ViterbiStateEntry *parent_vse); |
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204 |
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205 // Computes p(unichar | parent context) and records it in ngram_cost. |
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206 // If b.unichar_id() is an unlikely continuation of the parent context |
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207 // sets found_small_prob to true and returns nullptr. |
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208 // Otherwise creates a new LanguageModelNgramInfo entry containing the |
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209 // updated context (that includes b.unichar_id() at the end) and returns it. |
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210 // |
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211 // Note: the caller is responsible for deleting the returned pointer. |
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212 LanguageModelNgramInfo *GenerateNgramInfo(const char *unichar, float certainty, float denom, |
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213 int curr_col, int curr_row, float outline_length, |
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214 const ViterbiStateEntry *parent_vse); |
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215 |
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216 // Computes -(log(prob(classifier)) + log(prob(ngram model))) |
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217 // for the given unichar in the given context. If there are multiple |
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218 // unichars at one position - takes the average of their probabilities. |
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219 // UNICHAR::utf8_step() is used to separate out individual UTF8 characters, |
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220 // since probability_in_context() can only handle one at a time (while |
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221 // unicharset might contain ngrams and glyphs composed from multiple UTF8 |
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222 // characters). |
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223 float ComputeNgramCost(const char *unichar, float certainty, float denom, const char *context, |
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224 int *unichar_step_len, bool *found_small_prob, float *ngram_prob); |
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225 |
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226 // Computes the normalization factors for the classifier confidences |
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227 // (used by ComputeNgramCost()). |
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228 float ComputeDenom(BLOB_CHOICE_LIST *curr_list); |
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229 |
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230 // Fills the given consistency_info based on parent_vse.consistency_info |
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231 // and on the consistency of the given unichar_id with parent_vse. |
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232 void FillConsistencyInfo(int curr_col, bool word_end, BLOB_CHOICE *b, |
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233 ViterbiStateEntry *parent_vse, WERD_RES *word_res, |
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234 LMConsistencyInfo *consistency_info); |
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235 |
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236 // Constructs WERD_CHOICE by recording unichar_ids of the BLOB_CHOICEs |
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237 // on the path represented by the given BLOB_CHOICE and language model |
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238 // state entries (lmse, dse). The path is re-constructed by following |
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239 // the parent pointers in the lang model state entries). If the |
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240 // constructed WERD_CHOICE is better than the best/raw choice recorded |
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241 // in the best_choice_bundle, this function updates the corresponding |
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242 // fields and sets best_choice_bunldle->updated to true. |
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243 void UpdateBestChoice(ViterbiStateEntry *vse, LMPainPoints *pain_points, WERD_RES *word_res, |
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244 BestChoiceBundle *best_choice_bundle, BlamerBundle *blamer_bundle); |
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245 |
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246 // Constructs a WERD_CHOICE by tracing parent pointers starting with |
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247 // the given LanguageModelStateEntry. Returns the constructed word. |
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248 // Updates best_char_choices, certainties and state if they are not |
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249 // nullptr (best_char_choices and certainties are assumed to have the |
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250 // length equal to lmse->length). |
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251 // The caller is responsible for freeing memory associated with the |
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252 // returned WERD_CHOICE. |
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253 WERD_CHOICE *ConstructWord(ViterbiStateEntry *vse, WERD_RES *word_res, DANGERR *fixpt, |
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254 BlamerBundle *blamer_bundle, bool *truth_path); |
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255 |
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256 // Wrapper around AssociateUtils::ComputeStats(). |
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257 inline void ComputeAssociateStats(int col, int row, float max_char_wh_ratio, |
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258 ViterbiStateEntry *parent_vse, WERD_RES *word_res, |
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259 AssociateStats *associate_stats) { |
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260 AssociateUtils::ComputeStats( |
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261 col, row, (parent_vse != nullptr) ? &(parent_vse->associate_stats) : nullptr, |
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262 (parent_vse != nullptr) ? parent_vse->length : 0, fixed_pitch_, max_char_wh_ratio, word_res, |
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263 language_model_debug_level > 2, associate_stats); |
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264 } |
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265 |
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266 // Returns true if the path with such top_choice_flags and dawg_info |
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267 // could be pruned out (i.e. is neither a system/user/frequent dictionary |
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268 // nor a top choice path). |
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269 // In non-space delimited languages all paths can be "somewhat" dictionary |
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270 // words. In such languages we cannot do dictionary-driven path pruning, |
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271 // so paths with non-empty dawg_info are considered prunable. |
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272 inline bool PrunablePath(const ViterbiStateEntry &vse) { |
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273 if (vse.top_choice_flags) { |
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274 return false; |
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275 } |
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276 if (vse.dawg_info != nullptr && |
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277 (vse.dawg_info->permuter == SYSTEM_DAWG_PERM || vse.dawg_info->permuter == USER_DAWG_PERM || |
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278 vse.dawg_info->permuter == FREQ_DAWG_PERM)) { |
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279 return false; |
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280 } |
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281 return true; |
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282 } |
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283 |
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284 // Returns true if the given ViterbiStateEntry represents an acceptable path. |
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285 inline bool AcceptablePath(const ViterbiStateEntry &vse) { |
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286 return (vse.dawg_info != nullptr || vse.Consistent() || |
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287 (vse.ngram_info != nullptr && !vse.ngram_info->pruned)); |
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288 } |
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289 |
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290 public: |
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291 // Parameters. |
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292 INT_VAR_H(language_model_debug_level); |
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293 BOOL_VAR_H(language_model_ngram_on); |
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294 INT_VAR_H(language_model_ngram_order); |
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295 INT_VAR_H(language_model_viterbi_list_max_num_prunable); |
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296 INT_VAR_H(language_model_viterbi_list_max_size); |
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297 double_VAR_H(language_model_ngram_small_prob); |
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298 double_VAR_H(language_model_ngram_nonmatch_score); |
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299 BOOL_VAR_H(language_model_ngram_use_only_first_uft8_step); |
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300 double_VAR_H(language_model_ngram_scale_factor); |
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301 double_VAR_H(language_model_ngram_rating_factor); |
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302 BOOL_VAR_H(language_model_ngram_space_delimited_language); |
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303 INT_VAR_H(language_model_min_compound_length); |
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304 // Penalties used for adjusting path costs and final word rating. |
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305 double_VAR_H(language_model_penalty_non_freq_dict_word); |
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306 double_VAR_H(language_model_penalty_non_dict_word); |
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307 double_VAR_H(language_model_penalty_punc); |
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308 double_VAR_H(language_model_penalty_case); |
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309 double_VAR_H(language_model_penalty_script); |
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310 double_VAR_H(language_model_penalty_chartype); |
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311 double_VAR_H(language_model_penalty_font); |
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312 double_VAR_H(language_model_penalty_spacing); |
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313 double_VAR_H(language_model_penalty_increment); |
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314 INT_VAR_H(wordrec_display_segmentations); |
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315 BOOL_VAR_H(language_model_use_sigmoidal_certainty); |
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316 |
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317 protected: |
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318 // Member Variables. |
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319 |
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320 // Temporary DawgArgs struct that is re-used across different words to |
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321 // avoid dynamic memory re-allocation (should be cleared before each use). |
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322 DawgArgs dawg_args_; |
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323 // Scaling for recovering blob outline length from rating and certainty. |
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324 float rating_cert_scale_ = 0.0f; |
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325 |
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326 // The following variables are set at construction time. |
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327 |
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328 // Pointer to fontinfo table (not owned by LanguageModel). |
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329 const UnicityTable<FontInfo> *fontinfo_table_ = nullptr; |
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330 |
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331 // Pointer to Dict class, that is used for querying the dictionaries |
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332 // (the pointer is not owned by LanguageModel). |
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333 Dict *dict_ = nullptr; |
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334 |
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335 // TODO(daria): the following variables should become LanguageModel params |
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336 // when the old code in bestfirst.cpp and heuristic.cpp is deprecated. |
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337 // |
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338 // Set to true if we are dealing with fixed pitch text |
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339 // (set to assume_fixed_pitch_char_segment). |
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340 bool fixed_pitch_ = false; |
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341 // Max char width-to-height ratio allowed |
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342 // (set to segsearch_max_char_wh_ratio). |
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343 float max_char_wh_ratio_ = 0.0f; |
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344 |
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345 // The following variables are initialized with InitForWord(). |
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346 |
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347 // String representation of the classification of the previous word |
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348 // (since this is only used by the character ngram model component, |
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349 // only the last language_model_ngram_order of the word are stored). |
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350 std::string prev_word_str_; |
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351 int prev_word_unichar_step_len_ = 0; |
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352 // Active dawg vector. |
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353 DawgPositionVector very_beginning_active_dawgs_; // includes continuation |
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354 DawgPositionVector beginning_active_dawgs_; |
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355 // Set to true if acceptable choice was discovered. |
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356 // Note: it would be nice to use this to terminate the search once an |
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357 // acceptable choices is found. However we do not do that and once an |
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358 // acceptable choice is found we finish looking for alternative choices |
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359 // in the current segmentation graph and then exit the search (no more |
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360 // classifications are done after an acceptable choice is found). |
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361 // This is needed in order to let the search find the words very close to |
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362 // the best choice in rating (e.g. what/What, Cat/cat, etc) and log these |
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363 // choices. This way the stopper will know that the best choice is not |
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364 // ambiguous (i.e. there are best choices in the best choice list that have |
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365 // ratings close to the very best one) and will be less likely to mis-adapt. |
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366 bool acceptable_choice_found_ = false; |
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367 // Set to true if a choice representing correct segmentation was explored. |
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368 bool correct_segmentation_explored_ = false; |
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369 |
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370 // Params models containing weights for computing ViterbiStateEntry costs. |
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371 ParamsModel params_model_; |
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372 }; |
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373 |
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374 } // namespace tesseract |
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375 |
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376 #endif // TESSERACT_WORDREC_LANGUAGE_MODEL_H_ |
