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git-svn-id: https://tesseract-ocr.googlecode.com/svn/trunk@659 d0cd1f9f-072b-0410-8dd7-cf729c803f20
119 lines
4.7 KiB
C++
119 lines
4.7 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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// Raw features extracted from a single OCR hypothesis.
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// The features are non-normalized real-valued quantities with
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// unbounded range and 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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// kParamsTrainingRawFeatureTypeName enum accordingly.
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enum ParamsTrainingRawFeatureType {
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// What dictionary (if any) was this hypothesis found in.
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// See PermuterType enum in ccstruct/ratngs.h for interpretation.
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PTRAIN_RAW_FEATURE_DICT_MATCH_TYPE, // 0
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// Boolean indicator of whether this hypothesis is ambiguous to a known
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// dictionary word (or a valid number pattern).
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PTRAIN_RAW_FEATURE_UNAMBIG_DICT_MATCH, // 1
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// Shape cost of the segmentation path for this hypothesis.
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PTRAIN_RAW_FEATURE_SHAPE_COST, // 2
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// Character ngram probability of the string of unichars of this hypothesis.
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PTRAIN_RAW_FEATURE_NGRAM_PROB, // 3
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// Number of bad/inconsistent spots in this hypothesis.
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PTRAIN_RAW_FEATURE_NUM_BAD_PUNC, // 4
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PTRAIN_RAW_FEATURE_NUM_BAD_CASE, // 5
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PTRAIN_RAW_FEATURE_NUM_BAD_CHAR_TYPE, // 6
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PTRAIN_RAW_FEATURE_NUM_BAD_SPACING, // 7
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PTRAIN_RAW_FEATURE_NUM_BAD_SCRIPT, // 8
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PTRAIN_RAW_FEATURE_NUM_BAD_FONT, // 9
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// Classifier-related features.
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PTRAIN_RAW_FEATURE_WORST_CERT, // 10
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PTRAIN_RAW_FEATURE_RATING, // 11
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// Number of classifier results that came from adapted templates.
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PTRAIN_RAW_FEATURE_ADAPTED, // 12
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// Features potentially useful for normalization.
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PTRAIN_RAW_FEATURE_NUM_UNICHARS, // 13
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PTRAIN_RAW_FEATURE_OUTLINE_LEN, // 14
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PTRAIN_NUM_RAW_FEATURE_TYPES
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};
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static const char * const kParamsTrainingRawFeatureTypeName[] = {
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"DICT_MATCH_TYPE", // 0
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"UNAMBIG_DICT_MATCH", // 1
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"SHAPE_COST", // 2
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"NGRAM_PROB", // 3
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"NUM_BAD_PUNC", // 4
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"NUM_BAD_CASE", // 5
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"NUM_BAD_CHAR_TYPE", // 6
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"NUM_BAD_SPACING", // 7
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"NUM_BAD_SCRIPT", // 8
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"NUM_BAD_FONT", // 9
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"WORST_CERT", // 10
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"RATING", // 11
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"ADAPTED", // 12
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"NUM_UNICHARS", // 13
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"OUTLINE_LEN", // 14
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};
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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() {
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for (int i = 0; i < PTRAIN_NUM_RAW_FEATURE_TYPES; ++i) features[i] = 0.0;
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}
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float features[PTRAIN_NUM_RAW_FEATURE_TYPES];
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STRING str; // string corresponding to word hypothesis (for debugging)
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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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if (hyp_list_vec.empty()) StartHypothesisList();
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hyp_list_vec.back().push_back(ParamsTrainingHypothesis());
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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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