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https://github.com/tesseract-ocr/tesseract.git
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git-svn-id: https://tesseract-ocr.googlecode.com/svn/trunk@878 d0cd1f9f-072b-0410-8dd7-cf729c803f20
307 lines
12 KiB
C++
307 lines
12 KiB
C++
/**********************************************************************
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* File: tesseract_cube_combiner.h
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* Description: Declaration of the Tesseract & Cube results combiner Class
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* Author: Ahmad Abdulkader
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* Created: 2008
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*
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* (C) Copyright 2008, 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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// The TesseractCubeCombiner class provides the functionality of combining
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// the recognition results of Tesseract and Cube at the word level
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#include <algorithm>
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#include <string>
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#include <vector>
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#include <wctype.h>
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#include "tesseract_cube_combiner.h"
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#include "cube_object.h"
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#include "cube_reco_context.h"
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#include "cube_utils.h"
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#include "neural_net.h"
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#include "tesseractclass.h"
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#include "word_altlist.h"
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namespace tesseract {
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TesseractCubeCombiner::TesseractCubeCombiner(CubeRecoContext *cube_cntxt) {
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cube_cntxt_ = cube_cntxt;
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combiner_net_ = NULL;
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}
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TesseractCubeCombiner::~TesseractCubeCombiner() {
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if (combiner_net_ != NULL) {
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delete combiner_net_;
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combiner_net_ = NULL;
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}
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}
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bool TesseractCubeCombiner::LoadCombinerNet() {
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ASSERT_HOST(cube_cntxt_);
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// Compute the path of the combiner net
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string data_path;
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cube_cntxt_->GetDataFilePath(&data_path);
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string net_file_name = data_path + cube_cntxt_->Lang() +
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".tesseract_cube.nn";
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// Return false if file does not exist
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FILE *fp = fopen(net_file_name.c_str(), "rb");
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if (fp == NULL)
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return false;
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else
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fclose(fp);
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// Load and validate net
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combiner_net_ = NeuralNet::FromFile(net_file_name);
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if (combiner_net_ == NULL) {
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tprintf("Could not read combiner net file %s", net_file_name.c_str());
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return false;
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} else if (combiner_net_->out_cnt() != 2) {
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tprintf("Invalid combiner net file %s! Output count != 2\n",
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net_file_name.c_str());
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delete combiner_net_;
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combiner_net_ = NULL;
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return false;
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}
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return true;
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}
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// Normalize a UTF-8 string. Converts the UTF-8 string to UTF32 and optionally
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// strips punc and/or normalizes case and then converts back
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string TesseractCubeCombiner::NormalizeString(const string &str,
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bool remove_punc,
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bool norm_case) {
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// convert to UTF32
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string_32 str32;
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CubeUtils::UTF8ToUTF32(str.c_str(), &str32);
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// strip punc and normalize
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string_32 new_str32;
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for (int idx = 0; idx < str32.length(); idx++) {
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// if no punc removal is required or not a punctuation character
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if (!remove_punc || iswpunct(str32[idx]) == 0) {
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char_32 norm_char = str32[idx];
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// normalize case if required
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if (norm_case && iswalpha(norm_char)) {
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norm_char = towlower(norm_char);
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}
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new_str32.push_back(norm_char);
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}
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}
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// convert back to UTF8
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string new_str;
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CubeUtils::UTF32ToUTF8(new_str32.c_str(), &new_str);
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return new_str;
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}
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// Compares 2 strings optionally ignoring punctuation
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int TesseractCubeCombiner::CompareStrings(const string &str1,
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const string &str2,
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bool ignore_punc,
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bool ignore_case) {
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if (!ignore_punc && !ignore_case) {
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return str1.compare(str2);
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}
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string norm_str1 = NormalizeString(str1, ignore_punc, ignore_case);
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string norm_str2 = NormalizeString(str2, ignore_punc, ignore_case);
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return norm_str1.compare(norm_str2);
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}
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// Check if a string is a valid Tess dict word or not
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bool TesseractCubeCombiner::ValidWord(const string &str) {
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return (cube_cntxt_->TesseractObject()->getDict().valid_word(str.c_str())
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> 0);
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}
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// Public method for computing the combiner features. The agreement
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// output parameter will be true if both answers are identical,
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// and false otherwise.
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bool TesseractCubeCombiner::ComputeCombinerFeatures(const string &tess_str,
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int tess_confidence,
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CubeObject *cube_obj,
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WordAltList *cube_alt_list,
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vector<double> *features,
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bool *agreement) {
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features->clear();
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*agreement = false;
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if (cube_alt_list == NULL || cube_alt_list->AltCount() <= 0)
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return false;
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// Get Cube's best string; return false if empty
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char_32 *cube_best_str32 = cube_alt_list->Alt(0);
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if (cube_best_str32 == NULL || CubeUtils::StrLen(cube_best_str32) < 1)
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return false;
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string cube_best_str;
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int cube_best_cost = cube_alt_list->AltCost(0);
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int cube_best_bigram_cost = 0;
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bool cube_best_bigram_cost_valid = true;
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if (cube_cntxt_->Bigrams())
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cube_best_bigram_cost = cube_cntxt_->Bigrams()->
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Cost(cube_best_str32, cube_cntxt_->CharacterSet());
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else
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cube_best_bigram_cost_valid = false;
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CubeUtils::UTF32ToUTF8(cube_best_str32, &cube_best_str);
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// Get Tesseract's UTF32 string
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string_32 tess_str32;
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CubeUtils::UTF8ToUTF32(tess_str.c_str(), &tess_str32);
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// Compute agreement flag
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*agreement = (tess_str.compare(cube_best_str) == 0);
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// Get Cube's second best string; if empty, return false
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char_32 *cube_next_best_str32;
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string cube_next_best_str;
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int cube_next_best_cost = WORST_COST;
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if (cube_alt_list->AltCount() > 1) {
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cube_next_best_str32 = cube_alt_list->Alt(1);
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if (cube_next_best_str32 == NULL ||
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CubeUtils::StrLen(cube_next_best_str32) == 0) {
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return false;
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}
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cube_next_best_cost = cube_alt_list->AltCost(1);
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CubeUtils::UTF32ToUTF8(cube_next_best_str32, &cube_next_best_str);
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}
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// Rank of Tesseract's top result in Cube's alternate list
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int tess_rank = 0;
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for (tess_rank = 0; tess_rank < cube_alt_list->AltCount(); tess_rank++) {
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string alt_str;
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CubeUtils::UTF32ToUTF8(cube_alt_list->Alt(tess_rank), &alt_str);
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if (alt_str == tess_str)
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break;
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}
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// Cube's cost for tesseract's result. Note that this modifies the
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// state of cube_obj, including its alternate list by calling RecognizeWord()
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int tess_cost = cube_obj->WordCost(tess_str.c_str());
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// Cube's bigram cost of Tesseract's string
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int tess_bigram_cost = 0;
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int tess_bigram_cost_valid = true;
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if (cube_cntxt_->Bigrams())
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tess_bigram_cost = cube_cntxt_->Bigrams()->
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Cost(tess_str32.c_str(), cube_cntxt_->CharacterSet());
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else
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tess_bigram_cost_valid = false;
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// Tesseract confidence
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features->push_back(tess_confidence);
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// Cube cost of Tesseract string
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features->push_back(tess_cost);
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// Cube Rank of Tesseract string
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features->push_back(tess_rank);
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// length of Tesseract OCR string
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features->push_back(tess_str.length());
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// Tesseract OCR string in dictionary
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features->push_back(ValidWord(tess_str));
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if (tess_bigram_cost_valid) {
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// bigram cost of Tesseract string
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features->push_back(tess_bigram_cost);
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}
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// Cube tess_cost of Cube best string
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features->push_back(cube_best_cost);
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// Cube tess_cost of Cube next best string
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features->push_back(cube_next_best_cost);
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// length of Cube string
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features->push_back(cube_best_str.length());
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// Cube string in dictionary
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features->push_back(ValidWord(cube_best_str));
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if (cube_best_bigram_cost_valid) {
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// bigram cost of Cube string
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features->push_back(cube_best_bigram_cost);
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}
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// case-insensitive string comparison, including punctuation
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int compare_nocase_punc = CompareStrings(cube_best_str,
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tess_str, false, true);
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features->push_back(compare_nocase_punc == 0);
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// case-sensitive string comparison, ignoring punctuation
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int compare_case_nopunc = CompareStrings(cube_best_str,
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tess_str, true, false);
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features->push_back(compare_case_nopunc == 0);
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// case-insensitive string comparison, ignoring punctuation
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int compare_nocase_nopunc = CompareStrings(cube_best_str,
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tess_str, true, true);
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features->push_back(compare_nocase_nopunc == 0);
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return true;
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}
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// The CubeObject parameter is used for 2 purposes: 1) to retrieve
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// cube's alt list, and 2) to compute cube's word cost for the
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// tesseract result. The call to CubeObject::WordCost() modifies
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// the object's alternate list, so previous state will be lost.
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float TesseractCubeCombiner::CombineResults(WERD_RES *tess_res,
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CubeObject *cube_obj) {
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// If no combiner is loaded or the cube object is undefined,
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// tesseract wins with probability 1.0
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if (combiner_net_ == NULL || cube_obj == NULL) {
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tprintf("Cube WARNING (TesseractCubeCombiner::CombineResults): "
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"Cube objects not initialized; defaulting to Tesseract\n");
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return 1.0;
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}
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// Retrieve the alternate list from the CubeObject's current state.
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// If the alt list empty, tesseract wins with probability 1.0
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WordAltList *cube_alt_list = cube_obj->AlternateList();
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if (cube_alt_list == NULL)
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cube_alt_list = cube_obj->RecognizeWord();
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if (cube_alt_list == NULL || cube_alt_list->AltCount() <= 0) {
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tprintf("Cube WARNING (TesseractCubeCombiner::CombineResults): "
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"Cube returned no results; defaulting to Tesseract\n");
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return 1.0;
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}
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return CombineResults(tess_res, cube_obj, cube_alt_list);
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}
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// The alt_list parameter is expected to have been extracted from the
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// CubeObject that recognized the word to be combined. The cube_obj
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// parameter passed may be either same instance or a separate instance to
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// be used only by the combiner. In both cases, its alternate
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// list will be modified by an internal call to RecognizeWord().
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float TesseractCubeCombiner::CombineResults(WERD_RES *tess_res,
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CubeObject *cube_obj,
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WordAltList *cube_alt_list) {
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// If no combiner is loaded or the cube object is undefined, or the
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// alt list is empty, tesseract wins with probability 1.0
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if (combiner_net_ == NULL || cube_obj == NULL ||
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cube_alt_list == NULL || cube_alt_list->AltCount() <= 0) {
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tprintf("Cube WARNING (TesseractCubeCombiner::CombineResults): "
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"Cube result cannot be retrieved; defaulting to Tesseract\n");
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return 1.0;
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}
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// Tesseract result string, tesseract confidence, and cost of
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// tesseract result according to cube
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string tess_str = tess_res->best_choice->unichar_string().string();
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// Map certainty [-20.0, 0.0] to confidence [0, 100]
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int tess_confidence = MIN(100, MAX(1, static_cast<int>(
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100 + (5 * tess_res->best_choice->certainty()))));
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// Compute the combiner features. If feature computation fails or
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// answers are identical, tesseract wins with probability 1.0
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vector<double> features;
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bool agreement;
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bool combiner_success = ComputeCombinerFeatures(tess_str, tess_confidence,
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cube_obj, cube_alt_list,
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&features, &agreement);
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if (!combiner_success || agreement)
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return 1.0;
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// Classify combiner feature vector and return output (probability
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// of tesseract class).
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double net_out[2];
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if (!combiner_net_->FeedForward(&features[0], net_out))
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return 1.0;
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return net_out[1];
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}
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}
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