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git-svn-id: https://tesseract-ocr.googlecode.com/svn/trunk@878 d0cd1f9f-072b-0410-8dd7-cf729c803f20
150 lines
5.8 KiB
C++
150 lines
5.8 KiB
C++
///////////////////////////////////////////////////////////////////////
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// File: params_training_featdef.h
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// Description: Feature definitions for params training.
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// Author: Rika Antonova
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// Created: Mon Nov 28 11:26:42 PDT 2011
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//
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// (C) Copyright 2011, Google Inc.
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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// http://www.apache.org/licenses/LICENSE-2.0
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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//
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///////////////////////////////////////////////////////////////////////
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#ifndef TESSERACT_WORDREC_PARAMS_TRAINING_FEATDEF_H_
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#define TESSERACT_WORDREC_PARAMS_TRAINING_FEATDEF_H_
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#include "genericvector.h"
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#include "strngs.h"
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namespace tesseract {
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// Maximum number of unichars in the small and medium sized words
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static const int kMaxSmallWordUnichars = 3;
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static const int kMaxMediumWordUnichars = 6;
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// Raw features extracted from a single OCR hypothesis.
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// The features are normalized (by outline length or number of unichars as
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// appropriate) real-valued quantities with unbounded range and
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// unknown distribution.
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// Normalization / binarization of these features is done at a later stage.
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// Note: when adding new fields to this enum make sure to modify
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// kParamsTrainingFeatureTypeName
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enum kParamsTrainingFeatureType {
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// Digits
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PTRAIN_DIGITS_SHORT, // 0
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PTRAIN_DIGITS_MED, // 1
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PTRAIN_DIGITS_LONG, // 2
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// Number or pattern (NUMBER_PERM, USER_PATTERN_PERM)
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PTRAIN_NUM_SHORT, // 3
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PTRAIN_NUM_MED, // 4
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PTRAIN_NUM_LONG, // 5
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// Document word (DOC_DAWG_PERM)
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PTRAIN_DOC_SHORT, // 6
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PTRAIN_DOC_MED, // 7
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PTRAIN_DOC_LONG, // 8
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// Word (SYSTEM_DAWG_PERM, USER_DAWG_PERM, COMPOUND_PERM)
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PTRAIN_DICT_SHORT, // 9
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PTRAIN_DICT_MED, // 10
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PTRAIN_DICT_LONG, // 11
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// Frequent word (FREQ_DAWG_PERM)
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PTRAIN_FREQ_SHORT, // 12
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PTRAIN_FREQ_MED, // 13
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PTRAIN_FREQ_LONG, // 14
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PTRAIN_SHAPE_COST_PER_CHAR, // 15
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PTRAIN_NGRAM_COST_PER_CHAR, // 16
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PTRAIN_NUM_BAD_PUNC, // 17
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PTRAIN_NUM_BAD_CASE, // 18
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PTRAIN_XHEIGHT_CONSISTENCY, // 19
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PTRAIN_NUM_BAD_CHAR_TYPE, // 20
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PTRAIN_NUM_BAD_SPACING, // 21
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PTRAIN_NUM_BAD_FONT, // 22
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PTRAIN_RATING_PER_CHAR, // 23
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PTRAIN_NUM_FEATURE_TYPES
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};
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static const char * const kParamsTrainingFeatureTypeName[] = {
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"PTRAIN_DIGITS_SHORT", // 0
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"PTRAIN_DIGITS_MED", // 1
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"PTRAIN_DIGITS_LONG", // 2
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"PTRAIN_NUM_SHORT", // 3
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"PTRAIN_NUM_MED", // 4
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"PTRAIN_NUM_LONG", // 5
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"PTRAIN_DOC_SHORT", // 6
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"PTRAIN_DOC_MED", // 7
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"PTRAIN_DOC_LONG", // 8
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"PTRAIN_DICT_SHORT", // 9
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"PTRAIN_DICT_MED", // 10
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"PTRAIN_DICT_LONG", // 11
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"PTRAIN_FREQ_SHORT", // 12
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"PTRAIN_FREQ_MED", // 13
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"PTRAIN_FREQ_LONG", // 14
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"PTRAIN_SHAPE_COST_PER_CHAR", // 15
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"PTRAIN_NGRAM_COST_PER_CHAR", // 16
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"PTRAIN_NUM_BAD_PUNC", // 17
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"PTRAIN_NUM_BAD_CASE", // 18
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"PTRAIN_XHEIGHT_CONSISTENCY", // 19
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"PTRAIN_NUM_BAD_CHAR_TYPE", // 20
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"PTRAIN_NUM_BAD_SPACING", // 21
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"PTRAIN_NUM_BAD_FONT", // 22
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"PTRAIN_RATING_PER_CHAR", // 23
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};
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// Returns the index of the given feature (by name),
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// or -1 meaning the feature is unknown.
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int ParamsTrainingFeatureByName(const char *name);
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// Entry with features extracted from a single OCR hypothesis for a word.
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struct ParamsTrainingHypothesis {
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ParamsTrainingHypothesis() : cost(0.0) {
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memset(features, 0, sizeof(float) * PTRAIN_NUM_FEATURE_TYPES);
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}
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ParamsTrainingHypothesis(const ParamsTrainingHypothesis &other) {
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memcpy(features, other.features,
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sizeof(float) * PTRAIN_NUM_FEATURE_TYPES);
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str = other.str;
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cost = other.cost;
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}
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float features[PTRAIN_NUM_FEATURE_TYPES];
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STRING str; // string corresponding to word hypothesis (for debugging)
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float cost; // path cost computed by segsearch
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};
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// A list of hypotheses explored during one run of segmentation search.
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typedef GenericVector<ParamsTrainingHypothesis> ParamsTrainingHypothesisList;
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// A bundle that accumulates all of the hypothesis lists explored during all
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// of the runs of segmentation search on a word (e.g. a list of hypotheses
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// explored on PASS1, PASS2, fix xheight pass, etc).
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class ParamsTrainingBundle {
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public:
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ParamsTrainingBundle() {};
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// Starts a new hypothesis list.
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// Should be called at the beginning of a new run of the segmentation search.
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void StartHypothesisList() {
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hyp_list_vec.push_back(ParamsTrainingHypothesisList());
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}
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// Adds a new ParamsTrainingHypothesis to the current hypothesis list
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// and returns the reference to the newly added entry.
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ParamsTrainingHypothesis &AddHypothesis(
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const ParamsTrainingHypothesis &other) {
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if (hyp_list_vec.empty()) StartHypothesisList();
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hyp_list_vec.back().push_back(ParamsTrainingHypothesis(other));
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return hyp_list_vec.back().back();
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}
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GenericVector<ParamsTrainingHypothesisList> hyp_list_vec;
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};
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} // namespace tesseract
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#endif // TESSERACT_WORDREC_PARAMS_TRAINING_FEATDEF_H_
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