Mercurial > hgrepos > Python2 > PyMuPDF
annotate mupdf-source/thirdparty/tesseract/src/wordrec/wordrec.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: wordrec.h |
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3 // Description: wordrec class. |
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4 // Author: Samuel Charron |
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5 // |
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6 // (C) Copyright 2006, Google Inc. |
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7 // Licensed under the Apache License, Version 2.0 (the "License"); |
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8 // you may not use this file except in compliance with the License. |
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9 // You may obtain a copy of the License at |
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10 // http://www.apache.org/licenses/LICENSE-2.0 |
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11 // Unless required by applicable law or agreed to in writing, software |
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12 // distributed under the License is distributed on an "AS IS" BASIS, |
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13 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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14 // See the License for the specific language governing permissions and |
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15 // limitations under the License. |
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16 // |
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17 /////////////////////////////////////////////////////////////////////// |
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18 |
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19 #ifndef TESSERACT_WORDREC_WORDREC_H_ |
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20 #define TESSERACT_WORDREC_WORDREC_H_ |
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21 |
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22 #ifdef HAVE_CONFIG_H |
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23 # include "config_auto.h" // DISABLED_LEGACY_ENGINE |
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24 #endif |
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25 |
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26 #ifdef DISABLED_LEGACY_ENGINE |
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27 |
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28 # include <cstdint> // for int16_t, int32_t |
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29 # include "classify.h" // for Classify |
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30 # include "params.h" // for INT_VAR_H, IntParam, BOOL_VAR_H, BoolP... |
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31 # include "ratngs.h" // for WERD_CHOICE |
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32 |
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33 namespace tesseract { |
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34 class TessdataManager; |
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35 } |
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36 |
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37 namespace tesseract { |
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38 |
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39 /* ccmain/tstruct.cpp */ |
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40 |
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41 class TESS_API Wordrec : public Classify { |
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42 public: |
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43 // config parameters |
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44 |
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45 BOOL_VAR_H(wordrec_debug_blamer); |
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46 BOOL_VAR_H(wordrec_run_blamer); |
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47 |
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48 // methods |
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49 Wordrec(); |
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50 virtual ~Wordrec() = default; |
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51 |
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52 // tface.cpp |
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53 void program_editup(const std::string &textbase, TessdataManager *init_classifier, |
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54 TessdataManager *init_dict); |
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55 void program_editdown(int32_t elapsed_time); |
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56 int end_recog(); |
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57 int dict_word(const WERD_CHOICE &word); |
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58 |
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59 // Member variables |
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60 WERD_CHOICE *prev_word_best_choice_; |
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61 }; |
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62 |
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63 } // namespace tesseract |
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64 |
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65 #else // DISABLED_LEGACY_ENGINE not defined |
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66 |
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67 # include <memory> |
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68 # include "associate.h" |
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69 # include "chop.h" // for PointHeap, MAX_NUM_POINTS |
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70 # include "classify.h" // for Classify |
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71 # include "dict.h" |
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72 # include "elst.h" // for ELIST_ITERATOR, ELISTIZEH, ELIST_LINK |
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73 # include "findseam.h" // for SeamQueue, SeamPile |
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74 # include "language_model.h" |
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75 # include "matrix.h" |
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76 # include "oldlist.h" // for LIST |
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77 # include "params.h" // for INT_VAR_H, IntParam, BOOL_VAR_H, BoolP... |
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78 # include "points.h" // for ICOORD |
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79 # include "ratngs.h" // for BLOB_CHOICE_LIST (ptr only), BLOB_CHOI... |
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80 # include "seam.h" // for SEAM (ptr only), PRIORITY |
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81 # include "stopper.h" // for DANGERR |
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82 |
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83 # include <cstdint> // for int16_t, int32_t |
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84 |
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85 namespace tesseract { |
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86 |
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87 class EDGEPT_CLIST; |
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88 class MATRIX; |
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89 class TBOX; |
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90 class UNICHARSET; |
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91 class WERD_RES; |
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92 |
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93 class LMPainPoints; |
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94 class TessdataManager; |
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95 struct BestChoiceBundle; |
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96 |
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97 struct BlamerBundle; |
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98 struct EDGEPT; |
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99 struct MATRIX_COORD; |
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100 struct SPLIT; |
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101 struct TBLOB; |
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102 struct TESSLINE; |
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103 struct TWERD; |
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104 |
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105 // A class for storing which nodes are to be processed by the segmentation |
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106 // search. There is a single SegSearchPending for each column in the ratings |
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107 // matrix, and it indicates whether the segsearch should combine all |
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108 // BLOB_CHOICES in the column, or just the given row with the parents |
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109 // corresponding to *this SegSearchPending, and whether only updated parent |
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110 // ViterbiStateEntries should be combined, or all, with the BLOB_CHOICEs. |
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111 class SegSearchPending { |
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112 public: |
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113 SegSearchPending() |
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114 : classified_row_(-1), revisit_whole_column_(false), column_classified_(false) {} |
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115 |
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116 // Marks the whole column as just classified. Used to start a search on |
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117 // a newly initialized ratings matrix. |
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118 void SetColumnClassified() { |
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119 column_classified_ = true; |
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120 } |
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121 // Marks the matrix entry at the given row as just classified. |
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122 // Used after classifying a new matrix cell. |
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123 // Additional to, not overriding a previous RevisitWholeColumn. |
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124 void SetBlobClassified(int row) { |
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125 classified_row_ = row; |
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126 } |
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127 // Marks the whole column as needing work, but not just classified. |
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128 // Used when the parent vse list is updated. |
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129 // Additional to, not overriding a previous SetBlobClassified. |
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130 void RevisitWholeColumn() { |
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131 revisit_whole_column_ = true; |
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132 } |
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133 |
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134 // Clears *this to indicate no work to do. |
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135 void Clear() { |
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136 classified_row_ = -1; |
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137 revisit_whole_column_ = false; |
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138 column_classified_ = false; |
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139 } |
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140 |
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141 // Returns true if there are updates to do in the column that *this |
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142 // represents. |
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143 bool WorkToDo() const { |
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144 return revisit_whole_column_ || column_classified_ || classified_row_ >= 0; |
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145 } |
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146 // Returns true if the given row was just classified. |
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|
147 bool IsRowJustClassified(int row) const { |
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148 return row == classified_row_ || column_classified_; |
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149 } |
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150 // Returns the single row to process if there is only one, otherwise -1. |
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151 int SingleRow() const { |
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152 return revisit_whole_column_ || column_classified_ ? -1 : classified_row_; |
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153 } |
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154 |
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155 private: |
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156 // If non-negative, indicates the single row in the ratings matrix that has |
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157 // just been classified, and so should be combined with all the parents in the |
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158 // column that this SegSearchPending represents. |
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159 // Operates independently of revisit_whole_column. |
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160 int classified_row_; |
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161 // If revisit_whole_column is true, then all BLOB_CHOICEs in this column will |
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162 // be processed, but classified_row can indicate a row that is newly |
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163 // classified. Overridden if column_classified is true. |
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164 bool revisit_whole_column_; |
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165 // If column_classified is true, parent vses are processed with all rows |
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166 // regardless of whether they are just updated, overriding |
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167 // revisit_whole_column and classified_row. |
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168 bool column_classified_; |
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169 }; |
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170 |
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171 /* ccmain/tstruct.cpp *********************************************************/ |
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172 class FRAGMENT : public ELIST_LINK { |
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173 public: |
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174 FRAGMENT() { // constructor |
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175 } |
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176 FRAGMENT(EDGEPT *head_pt, // start |
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177 EDGEPT *tail_pt); // end |
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178 |
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179 ICOORD head; // coords of start |
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180 ICOORD tail; // coords of end |
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181 EDGEPT *headpt; // start point |
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182 EDGEPT *tailpt; // end point |
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183 }; |
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184 ELISTIZEH(FRAGMENT) |
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185 |
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186 class TESS_API Wordrec : public Classify { |
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187 public: |
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188 // config parameters ******************************************************* |
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189 BOOL_VAR_H(merge_fragments_in_matrix); |
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190 BOOL_VAR_H(wordrec_enable_assoc); |
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191 BOOL_VAR_H(force_word_assoc); |
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192 INT_VAR_H(repair_unchopped_blobs); |
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193 double_VAR_H(tessedit_certainty_threshold); |
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194 INT_VAR_H(chop_debug); |
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195 BOOL_VAR_H(chop_enable); |
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196 BOOL_VAR_H(chop_vertical_creep); |
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197 INT_VAR_H(chop_split_length); |
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198 INT_VAR_H(chop_same_distance); |
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199 INT_VAR_H(chop_min_outline_points); |
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200 INT_VAR_H(chop_seam_pile_size); |
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201 BOOL_VAR_H(chop_new_seam_pile); |
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202 INT_VAR_H(chop_inside_angle); |
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203 INT_VAR_H(chop_min_outline_area); |
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204 double_VAR_H(chop_split_dist_knob); |
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205 double_VAR_H(chop_overlap_knob); |
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206 double_VAR_H(chop_center_knob); |
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207 INT_VAR_H(chop_centered_maxwidth); |
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208 double_VAR_H(chop_sharpness_knob); |
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209 double_VAR_H(chop_width_change_knob); |
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210 double_VAR_H(chop_ok_split); |
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211 double_VAR_H(chop_good_split); |
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212 INT_VAR_H(chop_x_y_weight); |
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213 BOOL_VAR_H(assume_fixed_pitch_char_segment); |
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214 INT_VAR_H(wordrec_debug_level); |
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215 INT_VAR_H(wordrec_max_join_chunks); |
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216 BOOL_VAR_H(wordrec_skip_no_truth_words); |
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217 BOOL_VAR_H(wordrec_debug_blamer); |
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218 BOOL_VAR_H(wordrec_run_blamer); |
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219 INT_VAR_H(segsearch_debug_level); |
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220 INT_VAR_H(segsearch_max_pain_points); |
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221 INT_VAR_H(segsearch_max_futile_classifications); |
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222 double_VAR_H(segsearch_max_char_wh_ratio); |
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223 BOOL_VAR_H(save_alt_choices); |
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224 |
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225 // methods from wordrec/*.cpp *********************************************** |
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226 Wordrec(); |
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227 ~Wordrec() override = default; |
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228 |
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229 // Fills word->alt_choices with alternative paths found during |
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230 // chopping/segmentation search that are kept in best_choices. |
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231 void SaveAltChoices(const LIST &best_choices, WERD_RES *word); |
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232 |
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233 // Fills character choice lattice in the given BlamerBundle |
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234 // using the given ratings matrix and best choice list. |
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235 void FillLattice(const MATRIX &ratings, const WERD_CHOICE_LIST &best_choices, |
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236 const UNICHARSET &unicharset, BlamerBundle *blamer_bundle); |
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237 |
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238 // Calls fill_lattice_ member function |
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239 // (assumes that fill_lattice_ is not nullptr). |
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240 void CallFillLattice(const MATRIX &ratings, const WERD_CHOICE_LIST &best_choices, |
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241 const UNICHARSET &unicharset, BlamerBundle *blamer_bundle) { |
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242 (this->*fill_lattice_)(ratings, best_choices, unicharset, blamer_bundle); |
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243 } |
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244 |
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245 // tface.cpp |
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246 void program_editup(const std::string &textbase, TessdataManager *init_classifier, |
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247 TessdataManager *init_dict); |
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248 void cc_recog(WERD_RES *word); |
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249 void program_editdown(int32_t elapsed_time); |
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250 void set_pass1(); |
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251 void set_pass2(); |
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252 int end_recog(); |
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253 BLOB_CHOICE_LIST *call_matcher(TBLOB *blob); |
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254 int dict_word(const WERD_CHOICE &word); |
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255 // wordclass.cpp |
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256 BLOB_CHOICE_LIST *classify_blob(TBLOB *blob, const char *string, ScrollView::Color color, |
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257 BlamerBundle *blamer_bundle); |
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258 |
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259 // segsearch.cpp |
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260 // SegSearch works on the lower diagonal matrix of BLOB_CHOICE_LISTs. |
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261 // Each entry in the matrix represents the classification choice |
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262 // for a chunk, i.e. an entry in row 2, column 1 represents the list |
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263 // of ratings for the chunks 1 and 2 classified as a single blob. |
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264 // The entries on the diagonal of the matrix are classifier choice lists |
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265 // for a single chunk from the maximal segmentation. |
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266 // |
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267 // The ratings matrix given to SegSearch represents the segmentation |
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268 // graph / trellis for the current word. The nodes in the graph are the |
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269 // individual BLOB_CHOICEs in each of the BLOB_CHOICE_LISTs in the ratings |
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270 // matrix. The children of each node (nodes connected by outgoing links) |
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271 // are the entries in the column that is equal to node's row+1. The parents |
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272 // (nodes connected by the incoming links) are the entries in the row that |
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273 // is equal to the node's column-1. Here is an example ratings matrix: |
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274 // |
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275 // 0 1 2 3 4 |
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276 // ------------------------- |
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277 // 0| c,( | |
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278 // 1| d l,1 | |
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279 // 2| o | |
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280 // 3| c,( | |
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281 // 4| g,y l,1 | |
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282 // ------------------------- |
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283 // |
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284 // In the example above node "o" has children (outgoing connection to nodes) |
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285 // "c","(","g","y" and parents (incoming connections from nodes) "l","1","d". |
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286 // |
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287 // The objective of the search is to find the least cost path, where the cost |
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288 // is determined by the language model components and the properties of the |
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289 // cut between the blobs on the path. SegSearch starts by populating the |
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290 // matrix with the all the entries that were classified by the chopper and |
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291 // finding the initial best path. Based on the classifier ratings, language |
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292 // model scores and the properties of each cut, a list of "pain points" is |
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293 // constructed - those are the points on the path where the choices do not |
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294 // look consistent with the neighboring choices, the cuts look particularly |
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295 // problematic, or the certainties of the blobs are low. The most troublesome |
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296 // "pain point" is picked from the list and the new entry in the ratings |
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297 // matrix corresponding to this "pain point" is filled in. Then the language |
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298 // model state is updated to reflect the new classification and the new |
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299 // "pain points" are added to the list and the next most troublesome |
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300 // "pain point" is determined. This continues until either the word choice |
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301 // composed from the best paths in the segmentation graph is "good enough" |
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302 // (e.g. above a certain certainty threshold, is an unambiguous dictionary |
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303 // word, etc) or there are no more "pain points" to explore. |
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304 // |
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305 // If associate_blobs is set to false no new classifications will be done |
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306 // to combine blobs. Segmentation search will run only one "iteration" |
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307 // on the classifications already recorded in chunks_record.ratings. |
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308 // |
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309 // Note: this function assumes that word_res, best_choice_bundle arguments |
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310 // are not nullptr. |
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311 void SegSearch(WERD_RES *word_res, BestChoiceBundle *best_choice_bundle, |
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312 BlamerBundle *blamer_bundle); |
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313 |
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314 // Setup and run just the initial segsearch on an established matrix, |
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315 // without doing any additional chopping or joining. |
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316 // (Internal factored version that can be used as part of the main SegSearch.) |
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317 void InitialSegSearch(WERD_RES *word_res, LMPainPoints *pain_points, |
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318 std::vector<SegSearchPending> *pending, |
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319 BestChoiceBundle *best_choice_bundle, BlamerBundle *blamer_bundle); |
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320 |
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321 // chop.cpp |
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322 PRIORITY point_priority(EDGEPT *point); |
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323 void add_point_to_list(PointHeap *point_heap, EDGEPT *point); |
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324 // Returns true if the edgept supplied as input is an inside angle. This |
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325 // is determined by the angular change of the vectors from point to point. |
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326 bool is_inside_angle(EDGEPT *pt); |
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327 int angle_change(EDGEPT *point1, EDGEPT *point2, EDGEPT *point3); |
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328 EDGEPT *pick_close_point(EDGEPT *critical_point, EDGEPT *vertical_point, int *best_dist); |
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329 void prioritize_points(TESSLINE *outline, PointHeap *points); |
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330 void new_min_point(EDGEPT *local_min, PointHeap *points); |
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331 void new_max_point(EDGEPT *local_max, PointHeap *points); |
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332 void vertical_projection_point(EDGEPT *split_point, EDGEPT *target_point, EDGEPT **best_point, |
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333 EDGEPT_CLIST *new_points); |
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334 |
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335 // chopper.cpp |
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336 SEAM *attempt_blob_chop(TWERD *word, TBLOB *blob, int32_t blob_number, bool italic_blob, |
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337 const std::vector<SEAM *> &seams); |
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338 SEAM *chop_numbered_blob(TWERD *word, int32_t blob_number, bool italic_blob, |
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339 const std::vector<SEAM *> &seams); |
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340 SEAM *chop_overlapping_blob(const std::vector<TBOX> &boxes, bool italic_blob, WERD_RES *word_res, |
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341 unsigned *blob_number); |
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342 SEAM *improve_one_blob(const std::vector<BLOB_CHOICE *> &blob_choices, DANGERR *fixpt, |
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343 bool split_next_to_fragment, bool italic_blob, WERD_RES *word, |
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344 unsigned *blob_number); |
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345 SEAM *chop_one_blob(const std::vector<TBOX> &boxes, |
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346 const std::vector<BLOB_CHOICE *> &blob_choices, WERD_RES *word_res, |
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347 unsigned *blob_number); |
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348 void chop_word_main(WERD_RES *word); |
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349 void improve_by_chopping(float rating_cert_scale, WERD_RES *word, |
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350 BestChoiceBundle *best_choice_bundle, BlamerBundle *blamer_bundle, |
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351 LMPainPoints *pain_points, std::vector<SegSearchPending> *pending); |
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352 int select_blob_to_split(const std::vector<BLOB_CHOICE *> &blob_choices, float rating_ceiling, |
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353 bool split_next_to_fragment); |
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354 int select_blob_to_split_from_fixpt(DANGERR *fixpt); |
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355 |
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356 // findseam.cpp |
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357 void add_seam_to_queue(float new_priority, SEAM *new_seam, SeamQueue *seams); |
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358 void choose_best_seam(SeamQueue *seam_queue, const SPLIT *split, PRIORITY priority, |
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359 SEAM **seam_result, TBLOB *blob, SeamPile *seam_pile); |
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360 void combine_seam(const SeamPile &seam_pile, const SEAM *seam, SeamQueue *seam_queue); |
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361 SEAM *pick_good_seam(TBLOB *blob); |
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362 void try_point_pairs(EDGEPT *points[MAX_NUM_POINTS], int16_t num_points, SeamQueue *seam_queue, |
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363 SeamPile *seam_pile, SEAM **seam, TBLOB *blob); |
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364 void try_vertical_splits(EDGEPT *points[MAX_NUM_POINTS], int16_t num_points, |
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365 EDGEPT_CLIST *new_points, SeamQueue *seam_queue, SeamPile *seam_pile, |
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366 SEAM **seam, TBLOB *blob); |
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367 |
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368 // gradechop.cpp |
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369 PRIORITY grade_split_length(SPLIT *split); |
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370 PRIORITY grade_sharpness(SPLIT *split); |
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371 |
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372 // outlines.cpp |
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373 bool near_point(EDGEPT *point, EDGEPT *line_pt_0, EDGEPT *line_pt_1, EDGEPT **near_pt); |
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374 |
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375 // pieces.cpp |
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376 virtual BLOB_CHOICE_LIST *classify_piece(const std::vector<SEAM *> &seams, int16_t start, |
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377 int16_t end, const char *description, TWERD *word, |
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378 BlamerBundle *blamer_bundle); |
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379 |
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380 // Member variables. |
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381 |
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382 std::unique_ptr<LanguageModel> language_model_; |
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383 PRIORITY pass2_ok_split; |
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384 // Stores the best choice for the previous word in the paragraph. |
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385 // This variable is modified by PAGE_RES_IT when iterating over |
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386 // words to OCR on the page. |
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387 WERD_CHOICE *prev_word_best_choice_; |
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388 |
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389 // Function used to fill char choice lattices. |
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390 void (Wordrec::*fill_lattice_)(const MATRIX &ratings, const WERD_CHOICE_LIST &best_choices, |
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391 const UNICHARSET &unicharset, BlamerBundle *blamer_bundle); |
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392 |
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393 protected: |
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394 inline bool SegSearchDone(int num_futile_classifications) { |
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395 return (language_model_->AcceptableChoiceFound() || |
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396 num_futile_classifications >= segsearch_max_futile_classifications); |
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397 } |
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398 |
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399 // Updates the language model state recorded for the child entries specified |
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400 // in pending[starting_col]. Enqueues the children of the updated entries |
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401 // into pending and proceeds to update (and remove from pending) all the |
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402 // remaining entries in pending[col] (col >= starting_col). Upon termination |
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403 // of this function all the pending[col] lists will be empty. |
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404 // |
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405 // The arguments: |
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406 // |
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407 // starting_col: index of the column in chunks_record->ratings from |
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408 // which the update should be started |
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409 // |
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410 // pending: list of entries listing chunks_record->ratings entries |
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411 // that should be updated |
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412 // |
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413 // pain_points: priority heap listing the pain points generated by |
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414 // the language model |
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415 // |
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416 // temp_pain_points: temporary storage for tentative pain points generated |
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417 // by the language model after a single call to LanguageModel::UpdateState() |
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418 // (the argument is passed in rather than created before each |
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419 // LanguageModel::UpdateState() call to avoid dynamic memory re-allocation) |
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420 // |
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421 // best_choice_bundle: a collection of variables that should be updated |
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422 // if a new best choice is found |
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423 // |
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424 void UpdateSegSearchNodes(float rating_cert_scale, int starting_col, |
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425 std::vector<SegSearchPending> *pending, WERD_RES *word_res, |
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426 LMPainPoints *pain_points, BestChoiceBundle *best_choice_bundle, |
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427 BlamerBundle *blamer_bundle); |
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428 |
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429 // Process the given pain point: classify the corresponding blob, enqueue |
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430 // new pain points to join the newly classified blob with its neighbors. |
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431 void ProcessSegSearchPainPoint(float pain_point_priority, const MATRIX_COORD &pain_point, |
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432 const char *pain_point_type, |
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433 std::vector<SegSearchPending> *pending, WERD_RES *word_res, |
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434 LMPainPoints *pain_points, BlamerBundle *blamer_bundle); |
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435 // Resets enough of the results so that the Viterbi search is re-run. |
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436 // Needed when the n-gram model is enabled, as the multi-length comparison |
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437 // implementation will re-value existing paths to worse values. |
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438 void ResetNGramSearch(WERD_RES *word_res, BestChoiceBundle *best_choice_bundle, |
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439 std::vector<SegSearchPending> &pending); |
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440 |
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441 // Add pain points for classifying blobs on the correct segmentation path |
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442 // (so that we can evaluate correct segmentation path and discover the reason |
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443 // for incorrect result). |
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444 void InitBlamerForSegSearch(WERD_RES *word_res, LMPainPoints *pain_points, |
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445 BlamerBundle *blamer_bundle, std::string &blamer_debug); |
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446 }; |
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447 |
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448 } // namespace tesseract |
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449 |
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450 #endif // DISABLED_LEGACY_ENGINE |
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451 |
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452 #endif // TESSERACT_WORDREC_WORDREC_H_ |
