Mercurial > hgrepos > Python2 > PyMuPDF
annotate mupdf-source/thirdparty/tesseract/src/wordrec/segsearch.cpp @ 46:7ee69f120f19 default tip
>>>>> tag v1.26.5+1 for changeset b74429b0f5c4
| author | Franz Glasner <fzglas.hg@dom66.de> |
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| date | Sat, 11 Oct 2025 17:17:30 +0200 |
| parents | b50eed0cc0ef |
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| rev | line source |
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1 /////////////////////////////////////////////////////////////////////// |
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2 // File: segsearch.cpp |
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3 // Description: Segmentation search functions. |
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4 // Author: Daria Antonova |
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5 // |
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6 // (C) Copyright 2009, 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 #include <cstdint> // for INT32_MAX |
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20 #include "blamer.h" // for BlamerBundle |
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21 #include "errcode.h" // for ASSERT_HOST |
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22 #include "lm_pain_points.h" // for LMPainPoints, LM_PPTYPE_SHAPE, LMPainPoi... |
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23 #include "lm_state.h" // for BestChoiceBundle, ViterbiStateEntry |
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24 #include "matrix.h" // for MATRIX_COORD, MATRIX |
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25 #include "pageres.h" // for WERD_RES |
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26 #include "params.h" // for BoolParam, IntParam, DoubleParam |
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27 #include "ratngs.h" // for BLOB_CHOICE_LIST, BLOB_CHOICE_IT |
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28 #include "tprintf.h" // for tprintf |
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29 #include "wordrec.h" // for Wordrec, SegSearchPending (ptr only) |
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30 |
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31 namespace tesseract { |
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32 |
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33 void Wordrec::SegSearch(WERD_RES *word_res, BestChoiceBundle *best_choice_bundle, |
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34 BlamerBundle *blamer_bundle) { |
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35 LMPainPoints pain_points(segsearch_max_pain_points, segsearch_max_char_wh_ratio, |
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36 assume_fixed_pitch_char_segment, &getDict(), segsearch_debug_level); |
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37 // Compute scaling factor that will help us recover blob outline length |
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38 // from classifier rating and certainty for the blob. |
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39 float rating_cert_scale = -1.0 * getDict().certainty_scale / rating_scale; |
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40 std::vector<SegSearchPending> pending; |
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41 InitialSegSearch(word_res, &pain_points, &pending, best_choice_bundle, blamer_bundle); |
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42 |
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43 if (!SegSearchDone(0)) { // find a better choice |
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44 if (chop_enable && word_res->chopped_word != nullptr) { |
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45 improve_by_chopping(rating_cert_scale, word_res, best_choice_bundle, blamer_bundle, |
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46 &pain_points, &pending); |
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47 } |
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48 if (chop_debug) { |
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49 SEAM::PrintSeams("Final seam list:", word_res->seam_array); |
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50 } |
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51 |
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52 if (blamer_bundle != nullptr && !blamer_bundle->ChoiceIsCorrect(word_res->best_choice)) { |
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53 blamer_bundle->SetChopperBlame(word_res, wordrec_debug_blamer); |
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54 } |
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55 } |
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56 // Keep trying to find a better path by fixing the "pain points". |
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57 |
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58 MATRIX_COORD pain_point; |
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59 float pain_point_priority; |
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60 int num_futile_classifications = 0; |
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61 std::string blamer_debug; |
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62 while (wordrec_enable_assoc && |
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63 (!SegSearchDone(num_futile_classifications) || |
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64 (blamer_bundle != nullptr && blamer_bundle->GuidedSegsearchStillGoing()))) { |
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65 // Get the next valid "pain point". |
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66 bool found_nothing = true; |
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67 LMPainPointsType pp_type; |
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68 while ((pp_type = pain_points.Deque(&pain_point, &pain_point_priority)) != LM_PPTYPE_NUM) { |
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69 if (!pain_point.Valid(*word_res->ratings)) { |
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70 word_res->ratings->IncreaseBandSize(pain_point.row - pain_point.col + 1); |
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71 } |
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72 if (pain_point.Valid(*word_res->ratings) && |
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73 !word_res->ratings->Classified(pain_point.col, pain_point.row, getDict().WildcardID())) { |
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74 found_nothing = false; |
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75 break; |
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76 } |
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77 } |
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78 if (found_nothing) { |
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79 if (segsearch_debug_level > 0) { |
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80 tprintf("Pain points queue is empty\n"); |
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81 } |
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82 break; |
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83 } |
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84 ProcessSegSearchPainPoint(pain_point_priority, pain_point, |
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parents:
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|
85 LMPainPoints::PainPointDescription(pp_type), &pending, word_res, |
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|
86 &pain_points, blamer_bundle); |
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87 |
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88 UpdateSegSearchNodes(rating_cert_scale, pain_point.col, &pending, word_res, &pain_points, |
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89 best_choice_bundle, blamer_bundle); |
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90 if (!best_choice_bundle->updated) { |
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91 ++num_futile_classifications; |
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92 } |
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93 |
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94 if (segsearch_debug_level > 0) { |
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95 tprintf("num_futile_classifications %d\n", num_futile_classifications); |
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96 } |
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97 |
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98 best_choice_bundle->updated = false; // reset updated |
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99 |
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100 // See if it's time to terminate SegSearch or time for starting a guided |
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101 // search for the true path to find the blame for the incorrect best_choice. |
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102 if (SegSearchDone(num_futile_classifications) && blamer_bundle != nullptr && |
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103 blamer_bundle->GuidedSegsearchNeeded(word_res->best_choice)) { |
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104 InitBlamerForSegSearch(word_res, &pain_points, blamer_bundle, blamer_debug); |
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105 } |
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106 } // end while loop exploring alternative paths |
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107 if (blamer_bundle != nullptr) { |
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108 blamer_bundle->FinishSegSearch(word_res->best_choice, wordrec_debug_blamer, blamer_debug); |
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109 } |
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110 |
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111 if (segsearch_debug_level > 0) { |
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112 tprintf("Done with SegSearch (AcceptableChoiceFound: %d)\n", |
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113 language_model_->AcceptableChoiceFound()); |
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114 } |
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115 } |
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116 |
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117 // Setup and run just the initial segsearch on an established matrix, |
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118 // without doing any additional chopping or joining. |
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119 // (Internal factored version that can be used as part of the main SegSearch.) |
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120 void Wordrec::InitialSegSearch(WERD_RES *word_res, LMPainPoints *pain_points, |
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121 std::vector<SegSearchPending> *pending, |
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122 BestChoiceBundle *best_choice_bundle, BlamerBundle *blamer_bundle) { |
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123 if (segsearch_debug_level > 0) { |
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124 tprintf("Starting SegSearch on ratings matrix%s:\n", |
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125 wordrec_enable_assoc ? " (with assoc)" : ""); |
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126 word_res->ratings->print(getDict().getUnicharset()); |
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127 } |
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128 |
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129 pain_points->GenerateInitial(word_res); |
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130 |
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131 // Compute scaling factor that will help us recover blob outline length |
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132 // from classifier rating and certainty for the blob. |
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133 float rating_cert_scale = -1.0 * getDict().certainty_scale / rating_scale; |
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134 |
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|
135 language_model_->InitForWord(prev_word_best_choice_, assume_fixed_pitch_char_segment, |
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|
136 segsearch_max_char_wh_ratio, rating_cert_scale); |
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137 |
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138 // Initialize blamer-related information: map character boxes recorded in |
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139 // blamer_bundle->norm_truth_word to the corresponding i,j indices in the |
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140 // ratings matrix. We expect this step to succeed, since when running the |
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141 // chopper we checked that the correct chops are present. |
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|
142 if (blamer_bundle != nullptr) { |
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|
143 blamer_bundle->SetupCorrectSegmentation(word_res->chopped_word, wordrec_debug_blamer); |
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144 } |
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145 |
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146 // pending[col] tells whether there is update work to do to combine |
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147 // best_choice_bundle->beam[col - 1] with some BLOB_CHOICEs in matrix[col, *]. |
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148 // As the language model state is updated, pending entries are modified to |
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149 // minimize duplication of work. It is important that during the update the |
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150 // children are considered in the non-decreasing order of their column, since |
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151 // this guarantees that all the parents would be up to date before an update |
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152 // of a child is done. |
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153 pending->clear(); |
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154 pending->resize(word_res->ratings->dimension(), SegSearchPending()); |
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155 |
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156 // Search the ratings matrix for the initial best path. |
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|
157 (*pending)[0].SetColumnClassified(); |
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158 UpdateSegSearchNodes(rating_cert_scale, 0, pending, word_res, pain_points, best_choice_bundle, |
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159 blamer_bundle); |
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160 } |
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161 |
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|
162 void Wordrec::UpdateSegSearchNodes(float rating_cert_scale, int starting_col, |
|
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163 std::vector<SegSearchPending> *pending, WERD_RES *word_res, |
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164 LMPainPoints *pain_points, BestChoiceBundle *best_choice_bundle, |
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|
165 BlamerBundle *blamer_bundle) { |
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|
166 MATRIX *ratings = word_res->ratings; |
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167 ASSERT_HOST(static_cast<unsigned>(ratings->dimension()) == pending->size()); |
|
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|
168 ASSERT_HOST(static_cast<unsigned>(ratings->dimension()) == best_choice_bundle->beam.size()); |
|
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169 for (int col = starting_col; col < ratings->dimension(); ++col) { |
|
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|
170 if (!(*pending)[col].WorkToDo()) { |
|
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171 continue; |
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172 } |
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173 int first_row = col; |
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174 int last_row = std::min(ratings->dimension() - 1, col + ratings->bandwidth() - 1); |
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175 if ((*pending)[col].SingleRow() >= 0) { |
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176 first_row = last_row = (*pending)[col].SingleRow(); |
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177 } |
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178 if (segsearch_debug_level > 0) { |
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179 tprintf("\n\nUpdateSegSearchNodes: col=%d, rows=[%d,%d], alljust=%d\n", col, first_row, |
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180 last_row, (*pending)[col].IsRowJustClassified(INT32_MAX)); |
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181 } |
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182 // Iterate over the pending list for this column. |
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183 for (int row = first_row; row <= last_row; ++row) { |
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184 // Update language model state of this child+parent pair. |
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185 BLOB_CHOICE_LIST *current_node = ratings->get(col, row); |
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186 LanguageModelState *parent_node = col == 0 ? nullptr : best_choice_bundle->beam[col - 1]; |
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187 if (current_node != nullptr && |
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188 language_model_->UpdateState((*pending)[col].IsRowJustClassified(row), col, row, |
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189 current_node, parent_node, pain_points, word_res, |
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190 best_choice_bundle, blamer_bundle) && |
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191 row + 1 < ratings->dimension()) { |
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192 // Since the language model state of this entry changed, process all |
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193 // the child column. |
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194 (*pending)[row + 1].RevisitWholeColumn(); |
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195 if (segsearch_debug_level > 0) { |
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196 tprintf("Added child col=%d to pending\n", row + 1); |
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197 } |
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198 } // end if UpdateState. |
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199 } // end for row. |
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200 } // end for col. |
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|
201 if (best_choice_bundle->best_vse != nullptr) { |
|
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202 ASSERT_HOST(word_res->StatesAllValid()); |
|
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203 if (best_choice_bundle->best_vse->updated) { |
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204 pain_points->GenerateFromPath(rating_cert_scale, best_choice_bundle->best_vse, word_res); |
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|
205 if (!best_choice_bundle->fixpt.empty()) { |
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206 pain_points->GenerateFromAmbigs(best_choice_bundle->fixpt, best_choice_bundle->best_vse, |
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|
207 word_res); |
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208 } |
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209 } |
|
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210 } |
|
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211 // The segsearch is completed. Reset all updated flags on all VSEs and reset |
|
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212 // all pendings. |
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213 for (unsigned col = 0; col < pending->size(); ++col) { |
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214 (*pending)[col].Clear(); |
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215 ViterbiStateEntry_IT vse_it(&best_choice_bundle->beam[col]->viterbi_state_entries); |
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216 for (vse_it.mark_cycle_pt(); !vse_it.cycled_list(); vse_it.forward()) { |
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|
217 vse_it.data()->updated = false; |
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218 } |
|
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219 } |
|
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220 } |
|
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221 |
|
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|
222 void Wordrec::ProcessSegSearchPainPoint(float pain_point_priority, const MATRIX_COORD &pain_point, |
|
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|
223 const char *pain_point_type, |
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|
224 std::vector<SegSearchPending> *pending, |
|
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parents:
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225 WERD_RES *word_res, LMPainPoints *pain_points, |
|
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|
226 BlamerBundle *blamer_bundle) { |
|
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parents:
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|
227 if (segsearch_debug_level > 0) { |
|
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Franz Glasner <fzglas.hg@dom66.de>
parents:
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228 tprintf("Classifying pain point %s priority=%.4f, col=%d, row=%d\n", pain_point_type, |
|
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parents:
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|
229 pain_point_priority, pain_point.col, pain_point.row); |
|
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230 } |
|
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parents:
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|
231 ASSERT_HOST(pain_points != nullptr); |
|
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parents:
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|
232 MATRIX *ratings = word_res->ratings; |
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233 // Classify blob [pain_point.col pain_point.row] |
|
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|
234 if (!pain_point.Valid(*ratings)) { |
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235 ratings->IncreaseBandSize(pain_point.row + 1 - pain_point.col); |
|
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236 } |
|
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parents:
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|
237 ASSERT_HOST(pain_point.Valid(*ratings)); |
|
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parents:
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238 BLOB_CHOICE_LIST *classified = |
|
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239 classify_piece(word_res->seam_array, pain_point.col, pain_point.row, pain_point_type, |
|
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|
240 word_res->chopped_word, blamer_bundle); |
|
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parents:
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241 BLOB_CHOICE_LIST *lst = ratings->get(pain_point.col, pain_point.row); |
|
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|
242 if (lst == nullptr) { |
|
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Franz Glasner <fzglas.hg@dom66.de>
parents:
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|
243 ratings->put(pain_point.col, pain_point.row, classified); |
|
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|
244 } else { |
|
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parents:
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|
245 // We cannot delete old BLOB_CHOICEs, since they might contain |
|
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|
246 // ViterbiStateEntries that are parents of other "active" entries. |
|
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|
247 // Thus if the matrix cell already contains classifications we add |
|
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|
248 // the new ones to the beginning of the list. |
|
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parents:
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|
249 BLOB_CHOICE_IT it(lst); |
|
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Franz Glasner <fzglas.hg@dom66.de>
parents:
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|
250 it.add_list_before(classified); |
|
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Franz Glasner <fzglas.hg@dom66.de>
parents:
diff
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|
251 delete classified; // safe to delete, since empty after add_list_before() |
|
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|
252 classified = nullptr; |
|
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253 } |
|
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parents:
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|
254 |
|
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parents:
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|
255 if (segsearch_debug_level > 0) { |
|
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256 print_ratings_list("Updated ratings matrix with a new entry:", |
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257 ratings->get(pain_point.col, pain_point.row), getDict().getUnicharset()); |
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258 ratings->print(getDict().getUnicharset()); |
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259 } |
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260 |
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261 // Insert initial "pain points" to join the newly classified blob |
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262 // with its left and right neighbors. |
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263 if (classified != nullptr && !classified->empty()) { |
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264 if (pain_point.col > 0) { |
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265 pain_points->GeneratePainPoint(pain_point.col - 1, pain_point.row, LM_PPTYPE_SHAPE, 0.0, true, |
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266 segsearch_max_char_wh_ratio, word_res); |
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267 } |
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268 if (pain_point.row + 1 < ratings->dimension()) { |
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269 pain_points->GeneratePainPoint(pain_point.col, pain_point.row + 1, LM_PPTYPE_SHAPE, 0.0, true, |
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270 segsearch_max_char_wh_ratio, word_res); |
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271 } |
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272 } |
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273 (*pending)[pain_point.col].SetBlobClassified(pain_point.row); |
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274 } |
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275 |
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276 // Resets enough of the results so that the Viterbi search is re-run. |
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277 // Needed when the n-gram model is enabled, as the multi-length comparison |
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278 // implementation will re-value existing paths to worse values. |
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279 void Wordrec::ResetNGramSearch(WERD_RES *word_res, BestChoiceBundle *best_choice_bundle, |
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280 std::vector<SegSearchPending> &pending) { |
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281 // TODO(rays) More refactoring required here. |
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282 // Delete existing viterbi states. |
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283 for (auto &col : best_choice_bundle->beam) { |
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284 col->Clear(); |
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285 } |
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286 // Reset best_choice_bundle. |
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287 word_res->ClearWordChoices(); |
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288 best_choice_bundle->best_vse = nullptr; |
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289 // Clear out all existing pendings and add a new one for the first column. |
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290 pending[0].SetColumnClassified(); |
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291 for (auto &data : pending) { |
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292 data.Clear(); |
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293 } |
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294 } |
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295 |
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296 void Wordrec::InitBlamerForSegSearch(WERD_RES *word_res, LMPainPoints *pain_points, |
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297 BlamerBundle *blamer_bundle, std::string &blamer_debug) { |
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298 pain_points->Clear(); // Clear pain points heap. |
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299 blamer_bundle->InitForSegSearch(word_res->best_choice, word_res->ratings, getDict().WildcardID(), |
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300 wordrec_debug_blamer, blamer_debug, pain_points, |
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301 segsearch_max_char_wh_ratio, word_res); |
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302 } |
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303 |
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304 } // namespace tesseract |
