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0cfed22ed47e80c879661352a600db4593a8981c
deepin-ocr/3rdparty/tesseract_ocr/tesseract/src/ccstruct/blamer.cpp
wangcong 0cfed22ed4 feat: 集成Tesseract源码到项目中 
Description:   由于仓库中的Tesseract不是最新版本导致产生了一个bug，因此将Tesseract源码集成到项目中

Log: no
Change-Id: I088de95d6c6ab670406daa8d47ed2ed46929c2c0
2021-06-23 09:54:36 +08:00

579 lines
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///////////////////////////////////////////////////////////////////////
// File: blamer.cpp
// Description: Module allowing precise error causes to be allocated.
// Author: Rike Antonova
// Refactored: Ray Smith
//
// (C) Copyright 2013, 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.
//
///////////////////////////////////////////////////////////////////////
#include "blamer.h"
#include "blobs.h" // for TPOINT, TWERD, TBLOB
#include "errcode.h" // for ASSERT_HOST
#if !defined(DISABLED_LEGACY_ENGINE)
# include "lm_pain_points.h" // for LMPainPoints
#endif
#include "matrix.h" // for MATRIX
#include "normalis.h" // for DENORM
#include "pageres.h" // for WERD_RES
#include "unicharset.h" // for UNICHARSET
#include <cmath> // for abs
#include <cstdlib> // for abs
namespace tesseract {
// Names for each value of IncorrectResultReason enum. Keep in sync.
const char kBlameCorrect[] = "corr";
const char kBlameClassifier[] = "cl";
const char kBlameChopper[] = "chop";
const char kBlameClassLMTradeoff[] = "cl/LM";
const char kBlamePageLayout[] = "pglt";
const char kBlameSegsearchHeur[] = "ss_heur";
const char kBlameSegsearchPP[] = "ss_pp";
const char kBlameClassOldLMTradeoff[] = "cl/old_LM";
const char kBlameAdaption[] = "adapt";
const char kBlameNoTruthSplit[] = "no_tr_spl";
const char kBlameNoTruth[] = "no_tr";
const char kBlameUnknown[] = "unkn";
const char *const kIncorrectResultReasonNames[] = {
kBlameCorrect, kBlameClassifier, kBlameChopper, kBlameClassLMTradeoff,
kBlamePageLayout, kBlameSegsearchHeur, kBlameSegsearchPP, kBlameClassOldLMTradeoff,
kBlameAdaption, kBlameNoTruthSplit, kBlameNoTruth, kBlameUnknown};
const char *BlamerBundle::IncorrectReasonName(IncorrectResultReason irr) {
return kIncorrectResultReasonNames[irr];
}
const char *BlamerBundle::IncorrectReason() const {
return kIncorrectResultReasonNames[incorrect_result_reason_];
}
// Functions to setup the blamer.
// Whole word string, whole word bounding box.
void BlamerBundle::SetWordTruth(const UNICHARSET &unicharset, const char *truth_str,
const TBOX &word_box) {
truth_word_.InsertBox(0, word_box);
truth_has_char_boxes_ = false;
// Encode the string as UNICHAR_IDs.
std::vector<UNICHAR_ID> encoding;
std::vector<char> lengths;
unicharset.encode_string(truth_str, false, &encoding, &lengths, nullptr);
int total_length = 0;
for (int i = 0; i < encoding.size(); total_length += lengths[i++]) {
std::string uch(truth_str + total_length);
uch.resize(lengths[i] - total_length);
UNICHAR_ID id = encoding[i];
if (id != INVALID_UNICHAR_ID) {
uch = unicharset.get_normed_unichar(id);
}
truth_text_.push_back(uch);
}
}
// Single "character" string, "character" bounding box.
// May be called multiple times to indicate the characters in a word.
void BlamerBundle::SetSymbolTruth(const UNICHARSET &unicharset, const char *char_str,
const TBOX &char_box) {
std::string symbol_str(char_str);
UNICHAR_ID id = unicharset.unichar_to_id(char_str);
if (id != INVALID_UNICHAR_ID) {
std::string normed_uch(unicharset.get_normed_unichar(id));
if (normed_uch.length() > 0) {
symbol_str = normed_uch;
}
}
int length = truth_word_.length();
truth_text_.push_back(symbol_str);
truth_word_.InsertBox(length, char_box);
if (length == 0) {
truth_has_char_boxes_ = true;
} else if (truth_word_.BlobBox(length - 1) == char_box) {
truth_has_char_boxes_ = false;
}
}
// Marks that there is something wrong with the truth text, like it contains
// reject characters.
void BlamerBundle::SetRejectedTruth() {
incorrect_result_reason_ = IRR_NO_TRUTH;
truth_has_char_boxes_ = false;
}
// Returns true if the provided word_choice is correct.
bool BlamerBundle::ChoiceIsCorrect(const WERD_CHOICE *word_choice) const {
if (word_choice == nullptr) {
return false;
}
const UNICHARSET *uni_set = word_choice->unicharset();
std::string normed_choice_str;
for (int i = 0; i < word_choice->length(); ++i) {
normed_choice_str += uni_set->get_normed_unichar(word_choice->unichar_id(i));
}
std::string truth_str = TruthString();
return truth_str == normed_choice_str;
}
void BlamerBundle::FillDebugString(const std::string &msg, const WERD_CHOICE *choice, std::string &debug) {
debug += "Truth ";
for (auto &text : this->truth_text_) {
debug += text;
}
if (!this->truth_has_char_boxes_) {
debug += " (no char boxes)";
}
if (choice != nullptr) {
debug += " Choice ";
std::string choice_str;
choice->string_and_lengths(&choice_str, nullptr);
debug += choice_str;
}
if (msg.length() > 0) {
debug += "\n";
debug += msg;
}
debug += "\n";
}
// Sets up the norm_truth_word from truth_word using the given DENORM.
void BlamerBundle::SetupNormTruthWord(const DENORM &denorm) {
// TODO(rays) Is this the last use of denorm in WERD_RES and can it go?
norm_box_tolerance_ = kBlamerBoxTolerance * denorm.x_scale();
TPOINT topleft;
TPOINT botright;
TPOINT norm_topleft;
TPOINT norm_botright;
for (int b = 0; b < truth_word_.length(); ++b) {
const TBOX &box = truth_word_.BlobBox(b);
topleft.x = box.left();
topleft.y = box.top();
botright.x = box.right();
botright.y = box.bottom();
denorm.NormTransform(nullptr, topleft, &norm_topleft);
denorm.NormTransform(nullptr, botright, &norm_botright);
TBOX norm_box(norm_topleft.x, norm_botright.y, norm_botright.x, norm_topleft.y);
norm_truth_word_.InsertBox(b, norm_box);
}
}
// Splits *this into two pieces in bundle1 and bundle2 (preallocated, empty
// bundles) where the right edge/ of the left-hand word is word1_right,
// and the left edge of the right-hand word is word2_left.
void BlamerBundle::SplitBundle(int word1_right, int word2_left, bool debug, BlamerBundle *bundle1,
BlamerBundle *bundle2) const {
std::string debug_str;
// Find truth boxes that correspond to the split in the blobs.
int b;
int begin2_truth_index = -1;
if (incorrect_result_reason_ != IRR_NO_TRUTH && truth_has_char_boxes_) {
debug_str = "Looking for truth split at";
debug_str += " end1_x " + std::to_string(word1_right);
debug_str += " begin2_x " + std::to_string(word2_left);
debug_str += "\nnorm_truth_word boxes:\n";
if (norm_truth_word_.length() > 1) {
norm_truth_word_.BlobBox(0).print_to_str(debug_str);
for (b = 1; b < norm_truth_word_.length(); ++b) {
norm_truth_word_.BlobBox(b).print_to_str(debug_str);
if ((abs(word1_right - norm_truth_word_.BlobBox(b - 1).right()) < norm_box_tolerance_) &&
(abs(word2_left - norm_truth_word_.BlobBox(b).left()) < norm_box_tolerance_)) {
begin2_truth_index = b;
debug_str += "Split found";
break;
}
}
debug_str += '\n';
}
}
// Populate truth information in word and word2 with the first and second
// part of the original truth.
if (begin2_truth_index > 0) {
bundle1->truth_has_char_boxes_ = true;
bundle1->norm_box_tolerance_ = norm_box_tolerance_;
bundle2->truth_has_char_boxes_ = true;
bundle2->norm_box_tolerance_ = norm_box_tolerance_;
BlamerBundle *curr_bb = bundle1;
for (b = 0; b < norm_truth_word_.length(); ++b) {
if (b == begin2_truth_index) {
curr_bb = bundle2;
}
curr_bb->norm_truth_word_.InsertBox(b, norm_truth_word_.BlobBox(b));
curr_bb->truth_word_.InsertBox(b, truth_word_.BlobBox(b));
curr_bb->truth_text_.push_back(truth_text_[b]);
}
} else if (incorrect_result_reason_ == IRR_NO_TRUTH) {
bundle1->incorrect_result_reason_ = IRR_NO_TRUTH;
bundle2->incorrect_result_reason_ = IRR_NO_TRUTH;
} else {
debug_str += "Truth split not found";
debug_str += truth_has_char_boxes_ ? "\n" : " (no truth char boxes)\n";
bundle1->SetBlame(IRR_NO_TRUTH_SPLIT, debug_str, nullptr, debug);
bundle2->SetBlame(IRR_NO_TRUTH_SPLIT, debug_str, nullptr, debug);
}
}
// "Joins" the blames from bundle1 and bundle2 into *this.
void BlamerBundle::JoinBlames(const BlamerBundle &bundle1, const BlamerBundle &bundle2,
bool debug) {
std::string debug_str;
IncorrectResultReason irr = incorrect_result_reason_;
if (irr != IRR_NO_TRUTH_SPLIT) {
debug_str = "";
}
if (bundle1.incorrect_result_reason_ != IRR_CORRECT &&
bundle1.incorrect_result_reason_ != IRR_NO_TRUTH &&
bundle1.incorrect_result_reason_ != IRR_NO_TRUTH_SPLIT) {
debug_str += "Blame from part 1: ";
debug_str += bundle1.debug_;
irr = bundle1.incorrect_result_reason_;
}
if (bundle2.incorrect_result_reason_ != IRR_CORRECT &&
bundle2.incorrect_result_reason_ != IRR_NO_TRUTH &&
bundle2.incorrect_result_reason_ != IRR_NO_TRUTH_SPLIT) {
debug_str += "Blame from part 2: ";
debug_str += bundle2.debug_;
if (irr == IRR_CORRECT) {
irr = bundle2.incorrect_result_reason_;
} else if (irr != bundle2.incorrect_result_reason_) {
irr = IRR_UNKNOWN;
}
}
incorrect_result_reason_ = irr;
if (irr != IRR_CORRECT && irr != IRR_NO_TRUTH) {
SetBlame(irr, debug_str, nullptr, debug);
}
}
// If a blob with the same bounding box as one of the truth character
// bounding boxes is not classified as the corresponding truth character
// blames character classifier for incorrect answer.
void BlamerBundle::BlameClassifier(const UNICHARSET &unicharset, const TBOX &blob_box,
const BLOB_CHOICE_LIST &choices, bool debug) {
if (!truth_has_char_boxes_ || incorrect_result_reason_ != IRR_CORRECT) {
return; // Nothing to do here.
}
for (int b = 0; b < norm_truth_word_.length(); ++b) {
const TBOX &truth_box = norm_truth_word_.BlobBox(b);
// Note that we are more strict on the bounding box boundaries here
// than in other places (chopper, segmentation search), since we do
// not have the ability to check the previous and next bounding box.
if (blob_box.x_almost_equal(truth_box, norm_box_tolerance_ / 2)) {
bool found = false;
bool incorrect_adapted = false;
UNICHAR_ID incorrect_adapted_id = INVALID_UNICHAR_ID;
const char *truth_str = truth_text_[b].c_str();
// We promise not to modify the list or its contents, using a
// const BLOB_CHOICE* below.
BLOB_CHOICE_IT choices_it(const_cast<BLOB_CHOICE_LIST *>(&choices));
for (choices_it.mark_cycle_pt(); !choices_it.cycled_list(); choices_it.forward()) {
const BLOB_CHOICE *choice = choices_it.data();
if (strcmp(truth_str, unicharset.get_normed_unichar(choice->unichar_id())) == 0) {
found = true;
break;
} else if (choice->IsAdapted()) {
incorrect_adapted = true;
incorrect_adapted_id = choice->unichar_id();
}
} // end choices_it for loop
if (!found) {
std::string debug_str = "unichar ";
debug_str += truth_str;
debug_str += " not found in classification list";
SetBlame(IRR_CLASSIFIER, debug_str, nullptr, debug);
} else if (incorrect_adapted) {
std::string debug_str = "better rating for adapted ";
debug_str += unicharset.id_to_unichar(incorrect_adapted_id);
debug_str += " than for correct ";
debug_str += truth_str;
SetBlame(IRR_ADAPTION, debug_str, nullptr, debug);
}
break;
}
} // end iterating over blamer_bundle->norm_truth_word
}
// Checks whether chops were made at all the character bounding box
// boundaries in word->truth_word. If not - blames the chopper for an
// incorrect answer.
void BlamerBundle::SetChopperBlame(const WERD_RES *word, bool debug) {
if (NoTruth() || !truth_has_char_boxes_ || word->chopped_word->blobs.empty()) {
return;
}
std::string debug_str;
bool missing_chop = false;
int num_blobs = word->chopped_word->blobs.size();
int box_index = 0;
int blob_index = 0;
int16_t truth_x = -1;
while (box_index < truth_word_.length() && blob_index < num_blobs) {
truth_x = norm_truth_word_.BlobBox(box_index).right();
TBLOB *curr_blob = word->chopped_word->blobs[blob_index];
if (curr_blob->bounding_box().right() < truth_x - norm_box_tolerance_) {
++blob_index;
continue; // encountered an extra chop, keep looking
} else if (curr_blob->bounding_box().right() > truth_x + norm_box_tolerance_) {
missing_chop = true;
break;
} else {
++blob_index;
}
}
if (missing_chop || box_index < norm_truth_word_.length()) {
std::string debug_str;
if (missing_chop) {
debug_str += "Detected missing chop (tolerance=" + std::to_string(norm_box_tolerance_);
debug_str += ") at Bounding Box=";
TBLOB *curr_blob = word->chopped_word->blobs[blob_index];
curr_blob->bounding_box().print_to_str(debug_str);
debug_str += "\nNo chop for truth at x=" + std::to_string(truth_x);
} else {
debug_str += "Missing chops for last " + std::to_string(norm_truth_word_.length() - box_index);
debug_str += " truth box(es)";
}
debug_str += "\nMaximally chopped word boxes:\n";
for (blob_index = 0; blob_index < num_blobs; ++blob_index) {
TBLOB *curr_blob = word->chopped_word->blobs[blob_index];
curr_blob->bounding_box().print_to_str(debug_str);
debug_str += '\n';
}
debug_str += "Truth bounding boxes:\n";
for (box_index = 0; box_index < norm_truth_word_.length(); ++box_index) {
norm_truth_word_.BlobBox(box_index).print_to_str(debug_str);
debug_str += '\n';
}
SetBlame(IRR_CHOPPER, debug_str, word->best_choice, debug);
}
}
// Blames the classifier or the language model if, after running only the
// chopper, best_choice is incorrect and no blame has been yet set.
// Blames the classifier if best_choice is classifier's top choice and is a
// dictionary word (i.e. language model could not have helped).
// Otherwise, blames the language model (formerly permuter word adjustment).
void BlamerBundle::BlameClassifierOrLangModel(const WERD_RES *word, const UNICHARSET &unicharset,
bool valid_permuter, bool debug) {
if (valid_permuter) {
// Find out whether best choice is a top choice.
best_choice_is_dict_and_top_choice_ = true;
for (int i = 0; i < word->best_choice->length(); ++i) {
BLOB_CHOICE_IT blob_choice_it(word->GetBlobChoices(i));
ASSERT_HOST(!blob_choice_it.empty());
BLOB_CHOICE *first_choice = nullptr;
for (blob_choice_it.mark_cycle_pt(); !blob_choice_it.cycled_list();
blob_choice_it.forward()) { // find first non-fragment choice
if (!(unicharset.get_fragment(blob_choice_it.data()->unichar_id()))) {
first_choice = blob_choice_it.data();
break;
}
}
ASSERT_HOST(first_choice != nullptr);
if (first_choice->unichar_id() != word->best_choice->unichar_id(i)) {
best_choice_is_dict_and_top_choice_ = false;
break;
}
}
}
std::string debug_str;
if (best_choice_is_dict_and_top_choice_) {
debug_str = "Best choice is: incorrect, top choice, dictionary word";
debug_str += " with permuter ";
debug_str += word->best_choice->permuter_name();
} else {
debug_str = "Classifier/Old LM tradeoff is to blame";
}
SetBlame(best_choice_is_dict_and_top_choice_ ? IRR_CLASSIFIER : IRR_CLASS_OLD_LM_TRADEOFF,
debug_str, word->best_choice, debug);
}
// Sets up the correct_segmentation_* to mark the correct bounding boxes.
void BlamerBundle::SetupCorrectSegmentation(const TWERD *word, bool debug) {
#ifndef DISABLED_LEGACY_ENGINE
params_training_bundle_.StartHypothesisList();
#endif // ndef DISABLED_LEGACY_ENGINE
if (incorrect_result_reason_ != IRR_CORRECT || !truth_has_char_boxes_) {
return; // Nothing to do here.
}
std::string debug_str = "Blamer computing correct_segmentation_cols\n";
int curr_box_col = 0;
int next_box_col = 0;
int num_blobs = word->NumBlobs();
if (num_blobs == 0) {
return; // No blobs to play with.
}
int blob_index = 0;
int16_t next_box_x = word->blobs[blob_index]->bounding_box().right();
for (int truth_idx = 0; blob_index < num_blobs && truth_idx < norm_truth_word_.length();
++blob_index) {
++next_box_col;
int16_t curr_box_x = next_box_x;
if (blob_index + 1 < num_blobs) {
next_box_x = word->blobs[blob_index + 1]->bounding_box().right();
}
int16_t truth_x = norm_truth_word_.BlobBox(truth_idx).right();
debug_str += "Box x coord vs. truth: " + std::to_string(curr_box_x);
debug_str += " " + std::to_string(truth_x);
debug_str += "\n";
if (curr_box_x > (truth_x + norm_box_tolerance_)) {
break; // failed to find a matching box
} else if (curr_box_x >= truth_x - norm_box_tolerance_ && // matched
(blob_index + 1 >= num_blobs || // next box can't be included
next_box_x > truth_x + norm_box_tolerance_)) {
correct_segmentation_cols_.push_back(curr_box_col);
correct_segmentation_rows_.push_back(next_box_col - 1);
++truth_idx;
debug_str += "col=" + std::to_string(curr_box_col);
debug_str += " row=" + std::to_string(next_box_col - 1);
debug_str += "\n";
curr_box_col = next_box_col;
}
}
if (blob_index < num_blobs || // trailing blobs
correct_segmentation_cols_.size() != norm_truth_word_.length()) {
debug_str +=
"Blamer failed to find correct segmentation"
" (tolerance=" +
std::to_string(norm_box_tolerance_);
if (blob_index >= num_blobs) {
debug_str += " blob == nullptr";
}
debug_str += ")\n";
debug_str += " path length " + std::to_string(correct_segmentation_cols_.size());
debug_str += " vs. truth " + std::to_string(norm_truth_word_.length());
debug_str += "\n";
SetBlame(IRR_UNKNOWN, debug_str, nullptr, debug);
correct_segmentation_cols_.clear();
correct_segmentation_rows_.clear();
}
}
// Returns true if a guided segmentation search is needed.
bool BlamerBundle::GuidedSegsearchNeeded(const WERD_CHOICE *best_choice) const {
return incorrect_result_reason_ == IRR_CORRECT && !segsearch_is_looking_for_blame_ &&
truth_has_char_boxes_ && !ChoiceIsCorrect(best_choice);
}
#if !defined(DISABLED_LEGACY_ENGINE)
// Setup ready to guide the segmentation search to the correct segmentation.
void BlamerBundle::InitForSegSearch(const WERD_CHOICE *best_choice, MATRIX *ratings,
UNICHAR_ID wildcard_id, bool debug, std::string &debug_str,
tesseract::LMPainPoints *pain_points, double max_char_wh_ratio,
WERD_RES *word_res) {
segsearch_is_looking_for_blame_ = true;
if (debug) {
tprintf("segsearch starting to look for blame\n");
}
// Fill pain points for any unclassifed blob corresponding to the
// correct segmentation state.
debug_str += "Correct segmentation:\n";
for (int idx = 0; idx < correct_segmentation_cols_.size(); ++idx) {
debug_str += "col=" + std::to_string(correct_segmentation_cols_[idx]);
debug_str += " row=" + std::to_string(correct_segmentation_rows_[idx]);
debug_str += "\n";
if (!ratings->Classified(correct_segmentation_cols_[idx], correct_segmentation_rows_[idx],
wildcard_id) &&
!pain_points->GeneratePainPoint(
correct_segmentation_cols_[idx], correct_segmentation_rows_[idx],
tesseract::LM_PPTYPE_BLAMER, 0.0, false, max_char_wh_ratio, word_res)) {
segsearch_is_looking_for_blame_ = false;
debug_str += "\nFailed to insert pain point\n";
SetBlame(IRR_SEGSEARCH_HEUR, debug_str, best_choice, debug);
break;
}
} // end for blamer_bundle->correct_segmentation_cols/rows
}
#endif // !defined(DISABLED_LEGACY_ENGINE)
// Returns true if the guided segsearch is in progress.
bool BlamerBundle::GuidedSegsearchStillGoing() const {
return segsearch_is_looking_for_blame_;
}
// The segmentation search has ended. Sets the blame appropriately.
void BlamerBundle::FinishSegSearch(const WERD_CHOICE *best_choice, bool debug, std::string &debug_str) {
// If we are still looking for blame (i.e. best_choice is incorrect, but a
// path representing the correct segmentation could be constructed), we can
// blame segmentation search pain point prioritization if the rating of the
// path corresponding to the correct segmentation is better than that of
// best_choice (i.e. language model would have done the correct thing, but
// because of poor pain point prioritization the correct segmentation was
// never explored). Otherwise we blame the tradeoff between the language model
// and the classifier, since even after exploring the path corresponding to
// the correct segmentation incorrect best_choice would have been chosen.
// One special case when we blame the classifier instead is when best choice
// is incorrect, but it is a dictionary word and it classifier's top choice.
if (segsearch_is_looking_for_blame_) {
segsearch_is_looking_for_blame_ = false;
if (best_choice_is_dict_and_top_choice_) {
debug_str = "Best choice is: incorrect, top choice, dictionary word";
debug_str += " with permuter ";
debug_str += best_choice->permuter_name();
SetBlame(IRR_CLASSIFIER, debug_str, best_choice, debug);
} else if (best_correctly_segmented_rating_ < best_choice->rating()) {
debug_str += "Correct segmentation state was not explored";
SetBlame(IRR_SEGSEARCH_PP, debug_str, best_choice, debug);
} else {
if (best_correctly_segmented_rating_ >= WERD_CHOICE::kBadRating) {
debug_str += "Correct segmentation paths were pruned by LM\n";
} else {
debug_str += "Best correct segmentation rating " +
std::to_string(best_correctly_segmented_rating_);
debug_str += " vs. best choice rating " + std::to_string(best_choice->rating());
}
SetBlame(IRR_CLASS_LM_TRADEOFF, debug_str, best_choice, debug);
}
}
}
// If the bundle is null or still does not indicate the correct result,
// fix it and use some backup reason for the blame.
void BlamerBundle::LastChanceBlame(bool debug, WERD_RES *word) {
if (word->blamer_bundle == nullptr) {
word->blamer_bundle = new BlamerBundle();
word->blamer_bundle->SetBlame(IRR_PAGE_LAYOUT, "LastChanceBlame", word->best_choice, debug);
} else if (word->blamer_bundle->incorrect_result_reason_ == IRR_NO_TRUTH) {
word->blamer_bundle->SetBlame(IRR_NO_TRUTH, "Rejected truth", word->best_choice, debug);
} else {
bool correct = word->blamer_bundle->ChoiceIsCorrect(word->best_choice);
IncorrectResultReason irr = word->blamer_bundle->incorrect_result_reason_;
if (irr == IRR_CORRECT && !correct) {
std::string debug_str = "Choice is incorrect after recognition";
word->blamer_bundle->SetBlame(IRR_UNKNOWN, debug_str, word->best_choice, debug);
} else if (irr != IRR_CORRECT && correct) {
if (debug) {
tprintf("Corrected %s\n", word->blamer_bundle->debug_.c_str());
}
word->blamer_bundle->incorrect_result_reason_ = IRR_CORRECT;
word->blamer_bundle->debug_ = "";
}
}
}
// Sets the misadaption debug if this word is incorrect, as this word is
// being adapted to.
void BlamerBundle::SetMisAdaptionDebug(const WERD_CHOICE *best_choice, bool debug) {
if (incorrect_result_reason_ != IRR_NO_TRUTH && !ChoiceIsCorrect(best_choice)) {
misadaption_debug_ = "misadapt to word (";
misadaption_debug_ += best_choice->permuter_name();
misadaption_debug_ += "): ";
FillDebugString("", best_choice, misadaption_debug_);
if (debug) {
tprintf("%s\n", misadaption_debug_.c_str());
}
}
}
} // namespace tesseract
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