/usr/include/tesseract/params_training_featdef.h is in libtesseract-dev 3.02.01-6.
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// File: params_training_featdef.h
// Description: Feature definitions for params training.
// Author: Rika Antonova
// Created: Mon Nov 28 11:26:42 PDT 2011
//
// (C) Copyright 2011, Google Inc.
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
// http://www.apache.org/licenses/LICENSE-2.0
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
///////////////////////////////////////////////////////////////////////
#ifndef TESSERACT_WORDREC_PARAMS_TRAINING_FEATDEF_H_
#define TESSERACT_WORDREC_PARAMS_TRAINING_FEATDEF_H_
#include "genericvector.h"
#include "strngs.h"
namespace tesseract {
// Raw features extracted from a single OCR hypothesis.
// The features are non-normalized real-valued quantities with
// unbounded range and unknown distribution.
// Normalization / binarization of these features is done at a later stage.
// Note: when adding new fields to this enum make sure to modify
// kParamsTrainingRawFeatureTypeName enum accordingly.
enum ParamsTrainingRawFeatureType {
// What dictionary (if any) was this hypothesis found in.
// See PermuterType enum in ccstruct/ratngs.h for interpretation.
PTRAIN_RAW_FEATURE_DICT_MATCH_TYPE, // 0
// Boolean indicator of whether this hypothesis is ambiguous to a known
// dictionary word (or a valid number pattern).
PTRAIN_RAW_FEATURE_UNAMBIG_DICT_MATCH, // 1
// Shape cost of the segmentation path for this hypothesis.
PTRAIN_RAW_FEATURE_SHAPE_COST, // 2
// Character ngram probability of the string of unichars of this hypothesis.
PTRAIN_RAW_FEATURE_NGRAM_PROB, // 3
// Number of bad/inconsistent spots in this hypothesis.
PTRAIN_RAW_FEATURE_NUM_BAD_PUNC, // 4
PTRAIN_RAW_FEATURE_NUM_BAD_CASE, // 5
PTRAIN_RAW_FEATURE_NUM_BAD_CHAR_TYPE, // 6
PTRAIN_RAW_FEATURE_NUM_BAD_SPACING, // 7
PTRAIN_RAW_FEATURE_NUM_BAD_SCRIPT, // 8
PTRAIN_RAW_FEATURE_NUM_BAD_FONT, // 9
// Classifier-related features.
PTRAIN_RAW_FEATURE_WORST_CERT, // 10
PTRAIN_RAW_FEATURE_RATING, // 11
// Number of classifier results that came from adapted templates.
PTRAIN_RAW_FEATURE_ADAPTED, // 12
// Features potentially useful for normalization.
PTRAIN_RAW_FEATURE_NUM_UNICHARS, // 13
PTRAIN_RAW_FEATURE_OUTLINE_LEN, // 14
PTRAIN_NUM_RAW_FEATURE_TYPES
};
static const char * const kParamsTrainingRawFeatureTypeName[] = {
"DICT_MATCH_TYPE", // 0
"UNAMBIG_DICT_MATCH", // 1
"SHAPE_COST", // 2
"NGRAM_PROB", // 3
"NUM_BAD_PUNC", // 4
"NUM_BAD_CASE", // 5
"NUM_BAD_CHAR_TYPE", // 6
"NUM_BAD_SPACING", // 7
"NUM_BAD_SCRIPT", // 8
"NUM_BAD_FONT", // 9
"WORST_CERT", // 10
"RATING", // 11
"ADAPTED", // 12
"NUM_UNICHARS", // 13
"OUTLINE_LEN", // 14
};
// Entry with features extracted from a single OCR hypothesis for a word.
struct ParamsTrainingHypothesis {
ParamsTrainingHypothesis() {
for (int i = 0; i < PTRAIN_NUM_RAW_FEATURE_TYPES; ++i) features[i] = 0.0;
}
float features[PTRAIN_NUM_RAW_FEATURE_TYPES];
STRING str; // string corresponding to word hypothesis (for debugging)
};
// A list of hypotheses explored during one run of segmentation search.
typedef GenericVector<ParamsTrainingHypothesis> ParamsTrainingHypothesisList;
// A bundle that accumulates all of the hypothesis lists explored during all
// of the runs of segmentation search on a word (e.g. a list of hypotheses
// explored on PASS1, PASS2, fix xheight pass, etc).
class ParamsTrainingBundle {
public:
ParamsTrainingBundle() {};
// Starts a new hypothesis list.
// Should be called at the beginning of a new run of the segmentation search.
void StartHypothesisList() {
hyp_list_vec.push_back(ParamsTrainingHypothesisList());
}
// Adds a new ParamsTrainingHypothesis to the current hypothesis list
// and returns the reference to the newly added entry.
ParamsTrainingHypothesis &AddHypothesis() {
if (hyp_list_vec.empty()) StartHypothesisList();
hyp_list_vec.back().push_back(ParamsTrainingHypothesis());
return hyp_list_vec.back().back();
}
GenericVector<ParamsTrainingHypothesisList> hyp_list_vec;
};
} // namespace tesseract
#endif // TESSERACT_WORDREC_PARAMS_TRAINING_FEATDEF_H_
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