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comparison 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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1:1d09e1dec1d9 2:b50eed0cc0ef
1 ///////////////////////////////////////////////////////////////////////
2 // File: params_training_featdef.h
3 // Description: Feature definitions for params training.
4 // Author: Rika Antonova
5 //
6 // (C) Copyright 2011, Google Inc.
7 // Licensed under the Apache License, Version 2.0 (the "License");
8 // you may not use this file except in compliance with the License.
9 // You may obtain a copy of the License at
10 // http://www.apache.org/licenses/LICENSE-2.0
11 // Unless required by applicable law or agreed to in writing, software
12 // distributed under the License is distributed on an "AS IS" BASIS,
13 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 // See the License for the specific language governing permissions and
15 // limitations under the License.
16 //
17 ///////////////////////////////////////////////////////////////////////
18
19 #ifndef TESSERACT_WORDREC_PARAMS_TRAINING_FEATDEF_H_
20 #define TESSERACT_WORDREC_PARAMS_TRAINING_FEATDEF_H_
21
22 #include <cstring> // for memset
23 #include <string>
24 #include <vector>
25
26 namespace tesseract {
27
28 // Maximum number of unichars in the small and medium sized words
29 static const int kMaxSmallWordUnichars = 3;
30 static const int kMaxMediumWordUnichars = 6;
31
32 // Raw features extracted from a single OCR hypothesis.
33 // The features are normalized (by outline length or number of unichars as
34 // appropriate) real-valued quantities with unbounded range and
35 // unknown distribution.
36 // Normalization / binarization of these features is done at a later stage.
37 // Note: when adding new fields to this enum make sure to modify
38 // kParamsTrainingFeatureTypeName
39 enum kParamsTrainingFeatureType {
40 // Digits
41 PTRAIN_DIGITS_SHORT, // 0
42 PTRAIN_DIGITS_MED, // 1
43 PTRAIN_DIGITS_LONG, // 2
44 // Number or pattern (NUMBER_PERM, USER_PATTERN_PERM)
45 PTRAIN_NUM_SHORT, // 3
46 PTRAIN_NUM_MED, // 4
47 PTRAIN_NUM_LONG, // 5
48 // Document word (DOC_DAWG_PERM)
49 PTRAIN_DOC_SHORT, // 6
50 PTRAIN_DOC_MED, // 7
51 PTRAIN_DOC_LONG, // 8
52 // Word (SYSTEM_DAWG_PERM, USER_DAWG_PERM, COMPOUND_PERM)
53 PTRAIN_DICT_SHORT, // 9
54 PTRAIN_DICT_MED, // 10
55 PTRAIN_DICT_LONG, // 11
56 // Frequent word (FREQ_DAWG_PERM)
57 PTRAIN_FREQ_SHORT, // 12
58 PTRAIN_FREQ_MED, // 13
59 PTRAIN_FREQ_LONG, // 14
60 PTRAIN_SHAPE_COST_PER_CHAR, // 15
61 PTRAIN_NGRAM_COST_PER_CHAR, // 16
62 PTRAIN_NUM_BAD_PUNC, // 17
63 PTRAIN_NUM_BAD_CASE, // 18
64 PTRAIN_XHEIGHT_CONSISTENCY, // 19
65 PTRAIN_NUM_BAD_CHAR_TYPE, // 20
66 PTRAIN_NUM_BAD_SPACING, // 21
67 PTRAIN_NUM_BAD_FONT, // 22
68 PTRAIN_RATING_PER_CHAR, // 23
69
70 PTRAIN_NUM_FEATURE_TYPES
71 };
72
73 static const char *const kParamsTrainingFeatureTypeName[] = {
74 "PTRAIN_DIGITS_SHORT", // 0
75 "PTRAIN_DIGITS_MED", // 1
76 "PTRAIN_DIGITS_LONG", // 2
77 "PTRAIN_NUM_SHORT", // 3
78 "PTRAIN_NUM_MED", // 4
79 "PTRAIN_NUM_LONG", // 5
80 "PTRAIN_DOC_SHORT", // 6
81 "PTRAIN_DOC_MED", // 7
82 "PTRAIN_DOC_LONG", // 8
83 "PTRAIN_DICT_SHORT", // 9
84 "PTRAIN_DICT_MED", // 10
85 "PTRAIN_DICT_LONG", // 11
86 "PTRAIN_FREQ_SHORT", // 12
87 "PTRAIN_FREQ_MED", // 13
88 "PTRAIN_FREQ_LONG", // 14
89 "PTRAIN_SHAPE_COST_PER_CHAR", // 15
90 "PTRAIN_NGRAM_COST_PER_CHAR", // 16
91 "PTRAIN_NUM_BAD_PUNC", // 17
92 "PTRAIN_NUM_BAD_CASE", // 18
93 "PTRAIN_XHEIGHT_CONSISTENCY", // 19
94 "PTRAIN_NUM_BAD_CHAR_TYPE", // 20
95 "PTRAIN_NUM_BAD_SPACING", // 21
96 "PTRAIN_NUM_BAD_FONT", // 22
97 "PTRAIN_RATING_PER_CHAR", // 23
98 };
99
100 // Returns the index of the given feature (by name),
101 // or -1 meaning the feature is unknown.
102 int ParamsTrainingFeatureByName(const char *name);
103
104 // Entry with features extracted from a single OCR hypothesis for a word.
105 struct ParamsTrainingHypothesis {
106 ParamsTrainingHypothesis() : cost(0.0) {
107 memset(features, 0, sizeof(features));
108 }
109 ParamsTrainingHypothesis(const ParamsTrainingHypothesis &other) {
110 memcpy(features, other.features, sizeof(features));
111 str = other.str;
112 cost = other.cost;
113 }
114 ParamsTrainingHypothesis &operator=(const ParamsTrainingHypothesis &other) {
115 memcpy(features, other.features, sizeof(features));
116 str = other.str;
117 cost = other.cost;
118 return *this;
119 }
120 std::string str; // string corresponding to word hypothesis (for debugging)
121 float features[PTRAIN_NUM_FEATURE_TYPES];
122 float cost; // path cost computed by segsearch
123 };
124
125 // A list of hypotheses explored during one run of segmentation search.
126 using ParamsTrainingHypothesisList = std::vector<ParamsTrainingHypothesis>;
127
128 // A bundle that accumulates all of the hypothesis lists explored during all
129 // of the runs of segmentation search on a word (e.g. a list of hypotheses
130 // explored on PASS1, PASS2, fix xheight pass, etc).
131 class ParamsTrainingBundle {
132 public:
133 ParamsTrainingBundle() = default;
134 // Starts a new hypothesis list.
135 // Should be called at the beginning of a new run of the segmentation search.
136 void StartHypothesisList() {
137 hyp_list_vec.emplace_back();
138 }
139 // Adds a new ParamsTrainingHypothesis to the current hypothesis list
140 // and returns the reference to the newly added entry.
141 ParamsTrainingHypothesis &AddHypothesis(const ParamsTrainingHypothesis &other) {
142 if (hyp_list_vec.empty()) {
143 StartHypothesisList();
144 }
145 hyp_list_vec.back().push_back(ParamsTrainingHypothesis(other));
146 return hyp_list_vec.back().back();
147 }
148
149 std::vector<ParamsTrainingHypothesisList> hyp_list_vec;
150 };
151
152 } // namespace tesseract
153
154 #endif // TESSERACT_WORDREC_PARAMS_TRAINING_FEATDEF_H_