tesseract/ccmain/tesseract_cube_combiner.cpp

309 lines
12 KiB
C++

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