Mercurial > hgrepos > Python2 > PyMuPDF

annotate mupdf-source/thirdparty/tesseract/src/ccstruct/params_training_featdef.h @ 2:b50eed0cc0ef upstream

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ADD: MuPDF v1.26.7: the MuPDF source as downloaded by a default build of PyMuPDF 1.26.4. The directory name has changed: no version number in the expanded directory now.
author Franz Glasner <fzglas.hg@dom66.de>
date Mon, 15 Sep 2025 11:43:07 +0200
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children
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1 ///////////////////////////////////////////////////////////////////////
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2 // File: params_training_featdef.h
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3 // Description: Feature definitions for params training.
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4 // Author: Rika Antonova
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5 //
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6 // (C) Copyright 2011, 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_PARAMS_TRAINING_FEATDEF_H_
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20 #define TESSERACT_WORDREC_PARAMS_TRAINING_FEATDEF_H_
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21
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22 #include <cstring> // for memset
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23 #include <string>
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24 #include <vector>
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26 namespace tesseract {
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27
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28 // Maximum number of unichars in the small and medium sized words
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29 static const int kMaxSmallWordUnichars = 3;
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30 static const int kMaxMediumWordUnichars = 6;
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31
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32 // Raw features extracted from a single OCR hypothesis.
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33 // The features are normalized (by outline length or number of unichars as
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34 // appropriate) real-valued quantities with unbounded range and
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35 // unknown distribution.
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36 // Normalization / binarization of these features is done at a later stage.
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37 // Note: when adding new fields to this enum make sure to modify
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38 // kParamsTrainingFeatureTypeName
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39 enum kParamsTrainingFeatureType {
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40 // Digits
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41 PTRAIN_DIGITS_SHORT, // 0
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42 PTRAIN_DIGITS_MED, // 1
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43 PTRAIN_DIGITS_LONG, // 2
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44 // Number or pattern (NUMBER_PERM, USER_PATTERN_PERM)
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45 PTRAIN_NUM_SHORT, // 3
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46 PTRAIN_NUM_MED, // 4
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47 PTRAIN_NUM_LONG, // 5
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48 // Document word (DOC_DAWG_PERM)
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49 PTRAIN_DOC_SHORT, // 6
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50 PTRAIN_DOC_MED, // 7
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51 PTRAIN_DOC_LONG, // 8
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52 // Word (SYSTEM_DAWG_PERM, USER_DAWG_PERM, COMPOUND_PERM)
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53 PTRAIN_DICT_SHORT, // 9
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54 PTRAIN_DICT_MED, // 10
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55 PTRAIN_DICT_LONG, // 11
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56 // Frequent word (FREQ_DAWG_PERM)
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57 PTRAIN_FREQ_SHORT, // 12
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58 PTRAIN_FREQ_MED, // 13
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59 PTRAIN_FREQ_LONG, // 14
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60 PTRAIN_SHAPE_COST_PER_CHAR, // 15
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61 PTRAIN_NGRAM_COST_PER_CHAR, // 16
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62 PTRAIN_NUM_BAD_PUNC, // 17
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63 PTRAIN_NUM_BAD_CASE, // 18
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64 PTRAIN_XHEIGHT_CONSISTENCY, // 19
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65 PTRAIN_NUM_BAD_CHAR_TYPE, // 20
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66 PTRAIN_NUM_BAD_SPACING, // 21
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67 PTRAIN_NUM_BAD_FONT, // 22
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68 PTRAIN_RATING_PER_CHAR, // 23
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69
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70 PTRAIN_NUM_FEATURE_TYPES
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71 };
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72
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73 static const char *const kParamsTrainingFeatureTypeName[] = {
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74 "PTRAIN_DIGITS_SHORT", // 0
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75 "PTRAIN_DIGITS_MED", // 1
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76 "PTRAIN_DIGITS_LONG", // 2
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77 "PTRAIN_NUM_SHORT", // 3
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78 "PTRAIN_NUM_MED", // 4
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79 "PTRAIN_NUM_LONG", // 5
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80 "PTRAIN_DOC_SHORT", // 6
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81 "PTRAIN_DOC_MED", // 7
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82 "PTRAIN_DOC_LONG", // 8
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83 "PTRAIN_DICT_SHORT", // 9
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84 "PTRAIN_DICT_MED", // 10
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85 "PTRAIN_DICT_LONG", // 11
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86 "PTRAIN_FREQ_SHORT", // 12
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87 "PTRAIN_FREQ_MED", // 13
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88 "PTRAIN_FREQ_LONG", // 14
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89 "PTRAIN_SHAPE_COST_PER_CHAR", // 15
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90 "PTRAIN_NGRAM_COST_PER_CHAR", // 16
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91 "PTRAIN_NUM_BAD_PUNC", // 17
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92 "PTRAIN_NUM_BAD_CASE", // 18
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93 "PTRAIN_XHEIGHT_CONSISTENCY", // 19
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94 "PTRAIN_NUM_BAD_CHAR_TYPE", // 20
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95 "PTRAIN_NUM_BAD_SPACING", // 21
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96 "PTRAIN_NUM_BAD_FONT", // 22
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97 "PTRAIN_RATING_PER_CHAR", // 23
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98 };
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99
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100 // Returns the index of the given feature (by name),
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101 // or -1 meaning the feature is unknown.
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102 int ParamsTrainingFeatureByName(const char *name);
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103
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104 // Entry with features extracted from a single OCR hypothesis for a word.
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105 struct ParamsTrainingHypothesis {
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106 ParamsTrainingHypothesis() : cost(0.0) {
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107 memset(features, 0, sizeof(features));
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108 }
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109 ParamsTrainingHypothesis(const ParamsTrainingHypothesis &other) {
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110 memcpy(features, other.features, sizeof(features));
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111 str = other.str;
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112 cost = other.cost;
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113 }
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114 ParamsTrainingHypothesis &operator=(const ParamsTrainingHypothesis &other) {
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115 memcpy(features, other.features, sizeof(features));
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116 str = other.str;
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117 cost = other.cost;
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118 return *this;
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119 }
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120 std::string str; // string corresponding to word hypothesis (for debugging)
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121 float features[PTRAIN_NUM_FEATURE_TYPES];
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122 float cost; // path cost computed by segsearch
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123 };
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124
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125 // A list of hypotheses explored during one run of segmentation search.
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126 using ParamsTrainingHypothesisList = std::vector<ParamsTrainingHypothesis>;
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127
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128 // A bundle that accumulates all of the hypothesis lists explored during all
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129 // of the runs of segmentation search on a word (e.g. a list of hypotheses
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130 // explored on PASS1, PASS2, fix xheight pass, etc).
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131 class ParamsTrainingBundle {
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132 public:
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133 ParamsTrainingBundle() = default;
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134 // Starts a new hypothesis list.
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135 // Should be called at the beginning of a new run of the segmentation search.
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136 void StartHypothesisList() {
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137 hyp_list_vec.emplace_back();
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138 }
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139 // Adds a new ParamsTrainingHypothesis to the current hypothesis list
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140 // and returns the reference to the newly added entry.
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141 ParamsTrainingHypothesis &AddHypothesis(const ParamsTrainingHypothesis &other) {
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142 if (hyp_list_vec.empty()) {
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143 StartHypothesisList();
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144 }
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145 hyp_list_vec.back().push_back(ParamsTrainingHypothesis(other));
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146 return hyp_list_vec.back().back();
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147 }
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148
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149 std::vector<ParamsTrainingHypothesisList> hyp_list_vec;
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150 };
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151
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152 } // namespace tesseract
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153
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154 #endif // TESSERACT_WORDREC_PARAMS_TRAINING_FEATDEF_H_