Mercurial > hgrepos > Python2 > PyMuPDF
annotate mupdf-source/thirdparty/tesseract/src/wordrec/lm_state.h @ 21:2f43e400f144
Provide an "all" target to build both the sdist and the wheel
| author | Franz Glasner <fzglas.hg@dom66.de> |
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| date | Fri, 19 Sep 2025 10:28:53 +0200 |
| parents | b50eed0cc0ef |
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| rev | line source |
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1 /////////////////////////////////////////////////////////////////////// |
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2 // File: lm_state.h |
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3 // Description: Structures and functionality for capturing the state of |
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4 // segmentation search guided by the language model. |
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5 // Author: Rika Antonova |
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6 // |
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7 // (C) Copyright 2012, Google Inc. |
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8 // Licensed under the Apache License, Version 2.0 (the "License"); |
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9 // you may not use this file except in compliance with the License. |
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10 // You may obtain a copy of the License at |
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11 // http://www.apache.org/licenses/LICENSE-2.0 |
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12 // Unless required by applicable law or agreed to in writing, software |
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13 // distributed under the License is distributed on an "AS IS" BASIS, |
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14 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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15 // See the License for the specific language governing permissions and |
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16 // limitations under the License. |
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17 // |
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18 /////////////////////////////////////////////////////////////////////// |
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19 |
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20 #ifndef TESSERACT_WORDREC_LANGUAGE_MODEL_DEFS_H_ |
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21 #define TESSERACT_WORDREC_LANGUAGE_MODEL_DEFS_H_ |
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22 |
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23 #include <tesseract/unichar.h> // for UNICHAR_ID |
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24 #include "associate.h" // for AssociateStats |
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25 #include "dawg.h" // for DawgPositionVector |
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26 #include "elst.h" // for ELIST_ITERATOR, ELISTIZEH, ELIST_LINK |
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27 #include "lm_consistency.h" // for LMConsistencyInfo |
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28 #include "ratngs.h" // for BLOB_CHOICE, PermuterType |
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29 #include "stopper.h" // for DANGERR |
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30 #include "unicharset.h" // for UNICHARSET |
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31 |
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32 namespace tesseract { |
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33 |
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34 /// Used for expressing various language model flags. |
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35 using LanguageModelFlagsType = unsigned char; |
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36 |
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37 /// The following structs are used for storing the state of the language model |
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38 /// in the segmentation search graph. In this graph the nodes are BLOB_CHOICEs |
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39 /// and the links are the relationships between the underlying blobs (see |
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40 /// segsearch.h for a more detailed description). |
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41 /// |
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42 /// Each of the BLOB_CHOICEs contains LanguageModelState struct, which has |
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43 /// a list of N best paths (list of ViterbiStateEntry) explored by the Viterbi |
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44 /// search leading up to and including this BLOB_CHOICE. |
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45 /// |
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46 /// Each ViterbiStateEntry contains information from various components of the |
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47 /// language model: dawgs in which the path is found, character ngram model |
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48 /// probability of the path, script/chartype/font consistency info, state for |
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49 /// language-specific heuristics (e.g. hyphenated and compound words, |
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50 /// lower/upper case preferences, etc). |
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51 /// |
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52 /// Each ViterbiStateEntry also contains the parent pointer, so that the path |
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53 /// that it represents (WERD_CHOICE) can be constructed by following these |
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54 /// parent pointers. |
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55 |
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56 /// Struct for storing additional information used by Dawg language model |
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57 /// component. It stores the set of active dawgs in which the sequence of |
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58 /// letters on a path can be found. |
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59 struct LanguageModelDawgInfo { |
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60 LanguageModelDawgInfo(const DawgPositionVector *a, PermuterType pt) |
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61 : active_dawgs(*a), permuter(pt) {} |
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62 DawgPositionVector active_dawgs; |
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63 PermuterType permuter; |
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64 }; |
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65 |
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66 /// Struct for storing additional information used by Ngram language model |
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67 /// component. |
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68 struct LanguageModelNgramInfo { |
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69 LanguageModelNgramInfo(const char *c, int l, bool p, float nc, float ncc) |
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70 : context(c) |
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71 , context_unichar_step_len(l) |
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72 , pruned(p) |
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73 , ngram_cost(nc) |
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74 , ngram_and_classifier_cost(ncc) {} |
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75 std::string context; ///< context string |
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76 /// Length of the context measured by advancing using UNICHAR::utf8_step() |
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77 /// (should be at most the order of the character ngram model used). |
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78 int context_unichar_step_len; |
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79 /// The paths with pruned set are pruned out from the perspective of the |
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80 /// character ngram model. They are explored further because they represent |
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81 /// a dictionary match or a top choice. Thus ngram_info is still computed |
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82 /// for them in order to calculate the combined cost. |
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83 bool pruned; |
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84 /// -ln(P_ngram_model(path)) |
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85 float ngram_cost; |
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86 /// -[ ln(P_classifier(path)) + scale_factor * ln(P_ngram_model(path)) ] |
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87 float ngram_and_classifier_cost; |
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88 }; |
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89 |
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90 /// Struct for storing the information about a path in the segmentation graph |
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91 /// explored by Viterbi search. |
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92 struct ViterbiStateEntry : public ELIST_LINK { |
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93 ViterbiStateEntry(ViterbiStateEntry *pe, BLOB_CHOICE *b, float c, float ol, |
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94 const LMConsistencyInfo &ci, const AssociateStats &as, |
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95 LanguageModelFlagsType tcf, LanguageModelDawgInfo *d, LanguageModelNgramInfo *n, |
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96 const char *debug_uch) |
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97 : curr_b(b) |
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98 , parent_vse(pe) |
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99 , competing_vse(nullptr) |
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100 , dawg_info(d) |
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101 , ngram_info(n) |
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102 , cost(c) |
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103 , ratings_sum(b->rating()) |
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104 , min_certainty(b->certainty()) |
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105 , adapted(b->IsAdapted()) |
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106 , length(1) |
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107 , outline_length(ol) |
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108 , consistency_info(ci) |
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109 , associate_stats(as) |
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110 , top_choice_flags(tcf) |
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111 , updated(true) { |
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112 debug_str = (debug_uch == nullptr) ? nullptr : new std::string(); |
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113 if (pe != nullptr) { |
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114 ratings_sum += pe->ratings_sum; |
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115 if (pe->min_certainty < min_certainty) { |
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116 min_certainty = pe->min_certainty; |
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117 } |
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118 adapted += pe->adapted; |
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119 length += pe->length; |
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120 outline_length += pe->outline_length; |
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121 if (debug_uch != nullptr) { |
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122 *debug_str += *(pe->debug_str); |
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123 } |
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124 } |
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125 if (debug_str != nullptr && debug_uch != nullptr) { |
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126 *debug_str += debug_uch; |
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127 } |
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128 } |
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129 ~ViterbiStateEntry() { |
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130 delete dawg_info; |
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131 delete ngram_info; |
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132 delete debug_str; |
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133 } |
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134 /// Comparator function for sorting ViterbiStateEntry_LISTs in |
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135 /// non-increasing order of costs. |
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136 static int Compare(const void *e1, const void *e2) { |
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137 const ViterbiStateEntry *ve1 = *static_cast<const ViterbiStateEntry *const *>(e1); |
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138 const ViterbiStateEntry *ve2 = *static_cast<const ViterbiStateEntry *const *>(e2); |
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139 return (ve1->cost < ve2->cost) ? -1 : 1; |
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140 } |
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141 inline bool Consistent() const { |
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142 if (dawg_info != nullptr && consistency_info.NumInconsistentCase() == 0) { |
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143 return true; |
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144 } |
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145 return consistency_info.Consistent(); |
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146 } |
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147 /// Returns true if this VSE has an alphanumeric character as its classifier |
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148 /// result. |
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149 bool HasAlnumChoice(const UNICHARSET &unicharset) { |
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150 if (curr_b == nullptr) { |
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151 return false; |
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152 } |
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153 UNICHAR_ID unichar_id = curr_b->unichar_id(); |
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154 if (unicharset.get_isalpha(unichar_id) || unicharset.get_isdigit(unichar_id)) { |
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155 return true; |
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156 } |
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157 return false; |
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158 } |
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159 void Print(const char *msg) const; |
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160 |
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161 /// Pointers to BLOB_CHOICE and parent ViterbiStateEntry (not owned by this). |
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162 BLOB_CHOICE *curr_b; |
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163 ViterbiStateEntry *parent_vse; |
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164 /// Pointer to a case-competing ViterbiStateEntry in the same list that |
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165 /// represents a path ending in the same letter of the opposite case. |
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166 ViterbiStateEntry *competing_vse; |
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167 |
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168 /// Extra information maintained by Dawg language model component |
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169 /// (owned by ViterbiStateEntry). |
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170 LanguageModelDawgInfo *dawg_info; |
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171 |
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172 /// Extra information maintained by Ngram language model component |
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173 /// (owned by ViterbiStateEntry). |
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174 LanguageModelNgramInfo *ngram_info; |
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175 |
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176 /// UTF8 string representing the path corresponding to this vse. |
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177 /// Populated only in when language_model_debug_level > 0. |
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178 std::string *debug_str; |
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179 |
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180 /// The cost is an adjusted ratings sum, that is adjusted by all the language |
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181 /// model components that use Viterbi search. |
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182 float cost; |
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183 |
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184 /// Various information about the characters on the path represented |
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185 /// by this ViterbiStateEntry. |
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186 float ratings_sum; ///< sum of ratings of character on the path |
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187 float min_certainty; ///< minimum certainty on the path |
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188 int adapted; ///< number of BLOB_CHOICES from adapted templates |
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189 int length; ///< number of characters on the path |
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190 float outline_length; ///< length of the outline so far |
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191 LMConsistencyInfo consistency_info; ///< path consistency info |
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192 AssociateStats associate_stats; ///< character widths/gaps/seams |
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193 |
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194 /// Flags for marking the entry as a top choice path with |
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195 /// the smallest rating or lower/upper case letters). |
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196 LanguageModelFlagsType top_choice_flags; |
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197 |
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198 bool updated; ///< set to true if the entry has just been created/updated |
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199 }; |
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200 |
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201 ELISTIZEH(ViterbiStateEntry) |
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202 |
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203 /// Struct to store information maintained by various language model components. |
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204 struct LanguageModelState { |
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205 LanguageModelState() |
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206 : viterbi_state_entries_prunable_length(0) |
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207 , viterbi_state_entries_prunable_max_cost(FLT_MAX) |
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208 , viterbi_state_entries_length(0) {} |
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209 ~LanguageModelState() = default; |
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210 |
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211 /// Clears the viterbi search state back to its initial conditions. |
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212 void Clear(); |
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213 |
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214 void Print(const char *msg); |
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215 |
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216 /// Storage for the Viterbi state. |
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217 ViterbiStateEntry_LIST viterbi_state_entries; |
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218 /// Number and max cost of prunable paths in viterbi_state_entries. |
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219 int viterbi_state_entries_prunable_length; |
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220 float viterbi_state_entries_prunable_max_cost; |
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221 /// Total number of entries in viterbi_state_entries. |
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222 int viterbi_state_entries_length; |
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223 }; |
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224 |
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225 /// Bundle together all the things pertaining to the best choice/state. |
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226 struct BestChoiceBundle { |
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227 explicit BestChoiceBundle(int matrix_dimension) : updated(false), best_vse(nullptr) { |
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228 beam.reserve(matrix_dimension); |
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229 for (int i = 0; i < matrix_dimension; ++i) { |
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230 beam.push_back(new LanguageModelState); |
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231 } |
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232 } |
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233 ~BestChoiceBundle() { |
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234 for (auto &state : beam) { |
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235 delete state; |
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236 } |
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237 } |
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238 |
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239 /// Flag to indicate whether anything was changed. |
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240 bool updated; |
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241 /// Places to try to fix the word suggested by ambiguity checking. |
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242 DANGERR fixpt; |
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243 /// The beam. One LanguageModelState containing a list of ViterbiStateEntry |
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244 /// per row in the ratings matrix containing all VSEs whose BLOB_CHOICE is |
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245 /// somewhere in the corresponding row. |
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246 std::vector<LanguageModelState *> beam; |
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247 /// Best ViterbiStateEntry and BLOB_CHOICE. |
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248 ViterbiStateEntry *best_vse; |
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249 }; |
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250 |
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251 } // namespace tesseract |
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252 |
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253 #endif // TESSERACT_WORDREC_LANGUAGE_MODEL_DEFS_H_ |
