tesseract  5.0.0
params_training_featdef.h
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1 // 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 //
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
40  // Digits
44  // Number or pattern (NUMBER_PERM, USER_PATTERN_PERM)
48  // Document word (DOC_DAWG_PERM)
52  // Word (SYSTEM_DAWG_PERM, USER_DAWG_PERM, COMPOUND_PERM)
56  // Frequent word (FREQ_DAWG_PERM)
69 
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.
107  memset(features, 0, sizeof(features));
108  }
110  memcpy(features, other.features, sizeof(features));
111  str = other.str;
112  cost = other.cost;
113  }
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)
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).
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.
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.
142  if (hyp_list_vec.empty()) {
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_
int ParamsTrainingFeatureByName(const char *name)
std::vector< ParamsTrainingHypothesis > ParamsTrainingHypothesisList
ParamsTrainingHypothesis(const ParamsTrainingHypothesis &other)
float features[PTRAIN_NUM_FEATURE_TYPES]
ParamsTrainingHypothesis & operator=(const ParamsTrainingHypothesis &other)
std::vector< ParamsTrainingHypothesisList > hyp_list_vec
ParamsTrainingHypothesis & AddHypothesis(const ParamsTrainingHypothesis &other)