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524a61452d
tesseract/ccmain/control.cpp
Jim O'Regan 524a61452d Doxygen 
Squashed commit from https://github.com/tesseract-ocr/tesseract/tree/more-doxygen
closes #14

Commits:
6317305  doxygen
9f42f69  doxygen
0fc4d52  doxygen
37b4b55  fix typo
bded8f1  some more doxy
020eb00  slight tweak
524666d  doxygenify
2a36a3e  doxygenify
229d218  doxygenify
7fd28ae  doxygenify
a8c64bc  doxygenify
f5d21b6  fix
5d8ede8  doxygenify
a58a4e0  language_model.cpp
fa85709  lm_pain_points.cpp lm_state.cpp
6418da3  merge
06190ba  Merge branch 'old_doxygen_merge' into more-doxygen
84acf08  Merge branch 'master' into more-doxygen
50fe1ff  pagewalk.cpp cube_reco_context.cpp
2982583  change to relative
192a24a  applybox.cpp, take one
8eeb053  delete docs for obsolete params
52e4c77  modernise classify/ocrfeatures.cpp
2a1cba6  modernise cutil/emalloc.cpp
773e006  silence doxygen warning
aeb1731  silence doxygen warning
f18387f  silence doxygen; new params are unused?
15ad6bd  doxygenify cutil/efio.cpp
c8b5dad  doxygenify cutil/danerror.cpp
784450f  the globals and exceptions parts are obsolete; remove
8bca324  doxygen classify/normfeat.cpp
9bcbe16  doxygen classify/normmatch.cpp
aa9a971  doxygen ccmain/cube_control.cpp
c083ff2  doxygen ccmain/cube_reco_context.cpp
f842850  params changed
5c94f12  doxygen ccmain/cubeclassifier.cpp
15ba750  case sensitive
f5c71d4  case sensitive
f85655b  doxygen classify/intproto.cpp
4bbc7aa  partial doxygen classify/mfx.cpp
dbb6041  partial doxygen classify/intproto.cpp
2aa72db  finish doxygen classify/intproto.cpp
0b8de99  doxygen training/mftraining.cpp
0b5b35c  partial doxygen ccstruct/coutln.cpp
b81c766  partial doxygen ccstruct/coutln.cpp
40fc415  finished? doxygen ccstruct/coutln.cpp
6e4165c  doxygen classify/clusttool.cpp
0267dec  doxygen classify/cutoffs.cpp
7f0c70c  doxygen classify/fpoint.cpp
512f3bd  ignore ~ files
5668a52  doxygen classify/intmatcher.cpp
84788d4  doxygen classify/kdtree.cpp
29f36ca  doxygen classify/mfoutline.cpp
40b94b1  silence doxygen warnings
6c511b9  doxygen classify/mfx.cpp
f9b4080  doxygen classify/outfeat.cpp
aa1df05  doxygen classify/picofeat.cpp
cc5f466  doxygen training/cntraining.cpp
cce044f  doxygen training/commontraining.cpp
167e216  missing param
9498383  renamed params
37eeac2  renamed param
d87b5dd  case
c8ee174  renamed params
b858db8  typo
4c2a838  h2 context?
81a2c0c  fix some param names; add some missing params, no docs
bcf8a4c  add some missing params, no docs
af77f86  add some missing params, no docs; fix some param names
01df24e  fix some params
6161056  fix some params
68508b6  fix some params
285aeb6  doxygen complains here no matter what
529bcfa  rm some missing params, typos
cd21226  rm some missing params, add some new ones
48a4bc2  fix params
c844628  missing param
312ce37  missing param; rename one
ec2fdec  missing param
05e15e0  missing params
d515858  change "<" to &lt; to make doxygen happy
b476a28  wrong place
2015-07-20 18:48:00 +01:00

2046 lines
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/******************************************************************
* File: control.cpp (Formerly control.c)
* Description: Module-independent matcher controller.
* Author: Ray Smith
* Created: Thu Apr 23 11:09:58 BST 1992
* ReHacked: Tue Sep 22 08:42:49 BST 1992 Phil Cheatle
*
* (C) Copyright 1992, Hewlett-Packard Ltd.
** 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 <string.h>
#include <math.h>
#ifdef __UNIX__
#include <assert.h>
#include <unistd.h>
#include <errno.h>
#endif
#include <ctype.h>
#include "ocrclass.h"
#include "werdit.h"
#include "drawfx.h"
#include "tessbox.h"
#include "tessvars.h"
#include "pgedit.h"
#include "reject.h"
#include "fixspace.h"
#include "docqual.h"
#include "control.h"
#include "output.h"
#include "callcpp.h"
#include "globals.h"
#include "sorthelper.h"
#include "tesseractclass.h"
// Include automatically generated configuration file if running autoconf.
#ifdef HAVE_CONFIG_H
#include "config_auto.h"
#endif
#define MIN_FONT_ROW_COUNT 8
#define MAX_XHEIGHT_DIFF 3
const char* const kBackUpConfigFile = "tempconfigdata.config";
// Multiple of x-height to make a repeated word have spaces in it.
const double kRepcharGapThreshold = 0.5;
// Min believable x-height for any text when refitting as a fraction of
// original x-height
const double kMinRefitXHeightFraction = 0.5;
/**
* Make a word from the selected blobs and run Tess on them.
*
* @param page_res recognise blobs
* @param selection_box within this box
*/
namespace tesseract {
void Tesseract::recog_pseudo_word(PAGE_RES* page_res,
TBOX &selection_box) {
PAGE_RES_IT* it = make_pseudo_word(page_res, selection_box);
if (it != NULL) {
recog_interactive(it);
it->DeleteCurrentWord();
delete it;
}
}
/**
* Recognize a single word in interactive mode.
*
* @param pr_it the page results iterator
*/
BOOL8 Tesseract::recog_interactive(PAGE_RES_IT* pr_it) {
inT16 char_qual;
inT16 good_char_qual;
WordData word_data(*pr_it);
SetupWordPassN(2, &word_data);
classify_word_and_language(2, pr_it, &word_data);
if (tessedit_debug_quality_metrics) {
WERD_RES* word_res = pr_it->word();
word_char_quality(word_res, pr_it->row()->row, &char_qual, &good_char_qual);
tprintf("\n%d chars; word_blob_quality: %d; outline_errs: %d; "
"char_quality: %d; good_char_quality: %d\n",
word_res->reject_map.length(),
word_blob_quality(word_res, pr_it->row()->row),
word_outline_errs(word_res), char_qual, good_char_qual);
}
return TRUE;
}
// Helper function to check for a target word and handle it appropriately.
// Inspired by Jetsoft's requirement to process only single words on pass2
// and beyond.
// If word_config is not null:
// If the word_box and target_word_box overlap, read the word_config file
// else reset to previous config data.
// return true.
// else
// If the word_box and target_word_box overlap or pass <= 1, return true.
// Note that this function uses a fixed temporary file for storing the previous
// configs, so it is neither thread-safe, nor process-safe, but the assumption
// is that it will only be used for one debug window at a time.
//
// Since this function is used for debugging (and not to change OCR results)
// set only debug params from the word config file.
bool Tesseract::ProcessTargetWord(const TBOX& word_box,
const TBOX& target_word_box,
const char* word_config,
int pass) {
if (word_config != NULL) {
if (word_box.major_overlap(target_word_box)) {
if (backup_config_file_ == NULL) {
backup_config_file_ = kBackUpConfigFile;
FILE* config_fp = fopen(backup_config_file_, "wb");
ParamUtils::PrintParams(config_fp, params());
fclose(config_fp);
ParamUtils::ReadParamsFile(word_config,
SET_PARAM_CONSTRAINT_DEBUG_ONLY,
params());
}
} else {
if (backup_config_file_ != NULL) {
ParamUtils::ReadParamsFile(backup_config_file_,
SET_PARAM_CONSTRAINT_DEBUG_ONLY,
params());
backup_config_file_ = NULL;
}
}
} else if (pass > 1 && !word_box.major_overlap(target_word_box)) {
return false;
}
return true;
}
/** If tesseract is to be run, sets the words up ready for it. */
void Tesseract::SetupAllWordsPassN(int pass_n,
const TBOX* target_word_box,
const char* word_config,
PAGE_RES* page_res,
GenericVector<WordData>* words) {
// Prepare all the words.
PAGE_RES_IT page_res_it(page_res);
for (page_res_it.restart_page(); page_res_it.word() != NULL;
page_res_it.forward()) {
if (target_word_box == NULL ||
ProcessTargetWord(page_res_it.word()->word->bounding_box(),
*target_word_box, word_config, 1)) {
words->push_back(WordData(page_res_it));
}
}
// Setup all the words for recognition with polygonal approximation.
for (int w = 0; w < words->size(); ++w) {
SetupWordPassN(pass_n, &(*words)[w]);
if (w > 0) (*words)[w].prev_word = &(*words)[w - 1];
}
}
// Sets up the single word ready for whichever engine is to be run.
void Tesseract::SetupWordPassN(int pass_n, WordData* word) {
if (pass_n == 1 || !word->word->done) {
if (pass_n == 1) {
word->word->SetupForRecognition(unicharset, this, BestPix(),
tessedit_ocr_engine_mode, NULL,
classify_bln_numeric_mode,
textord_use_cjk_fp_model,
poly_allow_detailed_fx,
word->row, word->block);
} else if (pass_n == 2) {
// TODO(rays) Should we do this on pass1 too?
word->word->caps_height = 0.0;
if (word->word->x_height == 0.0f)
word->word->x_height = word->row->x_height();
}
word->lang_words.truncate(0);
for (int s = 0; s <= sub_langs_.size(); ++s) {
// The sub_langs_.size() entry is for the master language.
Tesseract* lang_t = s < sub_langs_.size() ? sub_langs_[s] : this;
WERD_RES* word_res = new WERD_RES;
word_res->InitForRetryRecognition(*word->word);
word->lang_words.push_back(word_res);
// Cube doesn't get setup for pass2.
if (pass_n == 1 || lang_t->tessedit_ocr_engine_mode != OEM_CUBE_ONLY) {
word_res->SetupForRecognition(
lang_t->unicharset, lang_t, BestPix(),
lang_t->tessedit_ocr_engine_mode, NULL,
lang_t->classify_bln_numeric_mode,
lang_t->textord_use_cjk_fp_model,
lang_t->poly_allow_detailed_fx, word->row, word->block);
}
}
}
}
// Runs word recognition on all the words.
bool Tesseract::RecogAllWordsPassN(int pass_n, ETEXT_DESC* monitor,
PAGE_RES_IT* pr_it,
GenericVector<WordData>* words) {
// TODO(rays) Before this loop can be parallelized (it would yield a massive
// speed-up) all remaining member globals need to be converted to local/heap
// (eg set_pass1 and set_pass2) and an intermediate adaption pass needs to be
// added. The results will be significantly different with adaption on, and
// deterioration will need investigation.
pr_it->restart_page();
for (int w = 0; w < words->size(); ++w) {
WordData* word = &(*words)[w];
if (w > 0) word->prev_word = &(*words)[w - 1];
if (monitor != NULL) {
monitor->ocr_alive = TRUE;
if (pass_n == 1)
monitor->progress = 30 + 50 * w / words->size();
else
monitor->progress = 80 + 10 * w / words->size();
if (monitor->deadline_exceeded() ||
(monitor->cancel != NULL && (*monitor->cancel)(monitor->cancel_this,
words->size()))) {
// Timeout. Fake out the rest of the words.
for (; w < words->size(); ++w) {
(*words)[w].word->SetupFake(unicharset);
}
return false;
}
}
if (word->word->tess_failed) {
int s;
for (s = 0; s < word->lang_words.size() &&
word->lang_words[s]->tess_failed; ++s) {}
// If all are failed, skip it. Image words are skipped by this test.
if (s > word->lang_words.size()) continue;
}
// Sync pr_it with the wth WordData.
while (pr_it->word() != NULL && pr_it->word() != word->word)
pr_it->forward();
ASSERT_HOST(pr_it->word() != NULL);
bool make_next_word_fuzzy = false;
if (ReassignDiacritics(pass_n, pr_it, &make_next_word_fuzzy)) {
// Needs to be setup again to see the new outlines in the chopped_word.
SetupWordPassN(pass_n, word);
}
classify_word_and_language(pass_n, pr_it, word);
if (tessedit_dump_choices || debug_noise_removal) {
tprintf("Pass%d: %s [%s]\n", pass_n,
word->word->best_choice->unichar_string().string(),
word->word->best_choice->debug_string().string());
}
pr_it->forward();
if (make_next_word_fuzzy && pr_it->word() != NULL) {
pr_it->MakeCurrentWordFuzzy();
}
}
return true;
}
/**
* recog_all_words()
*
* Walk the page_res, recognizing all the words.
* If monitor is not null, it is used as a progress monitor/timeout/cancel.
* If dopasses is 0, all recognition passes are run,
* 1 just pass 1, 2 passes2 and higher.
* If target_word_box is not null, special things are done to words that
* overlap the target_word_box:
* if word_config is not null, the word config file is read for just the
* target word(s), otherwise, on pass 2 and beyond ONLY the target words
* are processed (Jetsoft modification.)
* Returns false if we cancelled prematurely.
*
* @param page_res page structure
* @param monitor progress monitor
* @param word_config word_config file
* @param target_word_box specifies just to extract a rectangle
* @param dopasses 0 - all, 1 just pass 1, 2 passes 2 and higher
*/
bool Tesseract::recog_all_words(PAGE_RES* page_res,
ETEXT_DESC* monitor,
const TBOX* target_word_box,
const char* word_config,
int dopasses) {
PAGE_RES_IT page_res_it(page_res);
if (tessedit_minimal_rej_pass1) {
tessedit_test_adaption.set_value (TRUE);
tessedit_minimal_rejection.set_value (TRUE);
}
if (dopasses==0 || dopasses==1) {
page_res_it.restart_page();
// ****************** Pass 1 *******************
// If the adaptive classifier is full switch to one we prepared earlier,
// ie on the previous page. If the current adaptive classifier is non-empty,
// prepare a backup starting at this page, in case it fills up. Do all this
// independently for each language.
if (AdaptiveClassifierIsFull()) {
SwitchAdaptiveClassifier();
} else if (!AdaptiveClassifierIsEmpty()) {
StartBackupAdaptiveClassifier();
}
// Now check the sub-langs as well.
for (int i = 0; i < sub_langs_.size(); ++i) {
if (sub_langs_[i]->AdaptiveClassifierIsFull()) {
sub_langs_[i]->SwitchAdaptiveClassifier();
} else if (!sub_langs_[i]->AdaptiveClassifierIsEmpty()) {
sub_langs_[i]->StartBackupAdaptiveClassifier();
}
}
// Set up all words ready for recognition, so that if parallelism is on
// all the input and output classes are ready to run the classifier.
GenericVector<WordData> words;
SetupAllWordsPassN(1, target_word_box, word_config, page_res, &words);
if (tessedit_parallelize) {
PrerecAllWordsPar(words);
}
stats_.word_count = words.size();
stats_.dict_words = 0;
stats_.doc_blob_quality = 0;
stats_.doc_outline_errs = 0;
stats_.doc_char_quality = 0;
stats_.good_char_count = 0;
stats_.doc_good_char_quality = 0;
most_recently_used_ = this;
// Run pass 1 word recognition.
if (!RecogAllWordsPassN(1, monitor, &page_res_it, &words)) return false;
// Pass 1 post-processing.
for (page_res_it.restart_page(); page_res_it.word() != NULL;
page_res_it.forward()) {
if (page_res_it.word()->word->flag(W_REP_CHAR)) {
fix_rep_char(&page_res_it);
continue;
}
// Count dict words.
if (page_res_it.word()->best_choice->permuter() == USER_DAWG_PERM)
++(stats_.dict_words);
// Update misadaption log (we only need to do it on pass 1, since
// adaption only happens on this pass).
if (page_res_it.word()->blamer_bundle != NULL &&
page_res_it.word()->blamer_bundle->misadaption_debug().length() > 0) {
page_res->misadaption_log.push_back(
page_res_it.word()->blamer_bundle->misadaption_debug());
}
}
}
if (dopasses == 1) return true;
// ****************** Pass 2 *******************
if (tessedit_tess_adaption_mode != 0x0 && !tessedit_test_adaption &&
AnyTessLang()) {
page_res_it.restart_page();
GenericVector<WordData> words;
SetupAllWordsPassN(2, target_word_box, word_config, page_res, &words);
if (tessedit_parallelize) {
PrerecAllWordsPar(words);
}
most_recently_used_ = this;
// Run pass 2 word recognition.
if (!RecogAllWordsPassN(2, monitor, &page_res_it, &words)) return false;
}
// The next passes can only be run if tesseract has been used, as cube
// doesn't set all the necessary outputs in WERD_RES.
if (AnyTessLang()) {
// ****************** Pass 3 *******************
// Fix fuzzy spaces.
set_global_loc_code(LOC_FUZZY_SPACE);
if (!tessedit_test_adaption && tessedit_fix_fuzzy_spaces
&& !tessedit_word_for_word && !right_to_left())
fix_fuzzy_spaces(monitor, stats_.word_count, page_res);
// ****************** Pass 4 *******************
if (tessedit_enable_dict_correction) dictionary_correction_pass(page_res);
if (tessedit_enable_bigram_correction) bigram_correction_pass(page_res);
// ****************** Pass 5,6 *******************
rejection_passes(page_res, monitor, target_word_box, word_config);
#ifndef ANDROID_BUILD
// ****************** Pass 7 *******************
// Cube combiner.
// If cube is loaded and its combiner is present, run it.
if (tessedit_ocr_engine_mode == OEM_TESSERACT_CUBE_COMBINED) {
run_cube_combiner(page_res);
}
#endif
// ****************** Pass 8 *******************
font_recognition_pass(page_res);
// ****************** Pass 9 *******************
// Check the correctness of the final results.
blamer_pass(page_res);
script_pos_pass(page_res);
}
// Write results pass.
set_global_loc_code(LOC_WRITE_RESULTS);
// This is now redundant, but retained commented so show how to obtain
// bounding boxes and style information.
// changed by jetsoft
// needed for dll to output memory structure
if ((dopasses == 0 || dopasses == 2) && (monitor || tessedit_write_unlv))
output_pass(page_res_it, target_word_box);
// end jetsoft
PageSegMode pageseg_mode = static_cast<PageSegMode>(
static_cast<int>(tessedit_pageseg_mode));
textord_.CleanupSingleRowResult(pageseg_mode, page_res);
// Remove empty words, as these mess up the result iterators.
for (page_res_it.restart_page(); page_res_it.word() != NULL;
page_res_it.forward()) {
WERD_RES* word = page_res_it.word();
if (word->best_choice == NULL || word->best_choice->length() == 0)
page_res_it.DeleteCurrentWord();
}
if (monitor != NULL) {
monitor->progress = 100;
}
return true;
}
void Tesseract::bigram_correction_pass(PAGE_RES *page_res) {
PAGE_RES_IT word_it(page_res);
WERD_RES *w_prev = NULL;
WERD_RES *w = word_it.word();
while (1) {
w_prev = w;
while (word_it.forward() != NULL &&
(!word_it.word() || word_it.word()->part_of_combo)) {
// advance word_it, skipping over parts of combos
}
if (!word_it.word()) break;
w = word_it.word();
if (!w || !w_prev || w->uch_set != w_prev->uch_set) {
continue;
}
if (w_prev->word->flag(W_REP_CHAR) || w->word->flag(W_REP_CHAR)) {
if (tessedit_bigram_debug) {
tprintf("Skipping because one of the words is W_REP_CHAR\n");
}
continue;
}
// Two words sharing the same language model, excellent!
GenericVector<WERD_CHOICE *> overrides_word1;
GenericVector<WERD_CHOICE *> overrides_word2;
STRING orig_w1_str = w_prev->best_choice->unichar_string();
STRING orig_w2_str = w->best_choice->unichar_string();
WERD_CHOICE prev_best(w->uch_set);
{
int w1start, w1end;
w_prev->best_choice->GetNonSuperscriptSpan(&w1start, &w1end);
prev_best = w_prev->best_choice->shallow_copy(w1start, w1end);
}
WERD_CHOICE this_best(w->uch_set);
{
int w2start, w2end;
w->best_choice->GetNonSuperscriptSpan(&w2start, &w2end);
this_best = w->best_choice->shallow_copy(w2start, w2end);
}
if (w->tesseract->getDict().valid_bigram(prev_best, this_best)) {
if (tessedit_bigram_debug) {
tprintf("Top choice \"%s %s\" verified by bigram model.\n",
orig_w1_str.string(), orig_w2_str.string());
}
continue;
}
if (tessedit_bigram_debug > 2) {
tprintf("Examining alt choices for \"%s %s\".\n",
orig_w1_str.string(), orig_w2_str.string());
}
if (tessedit_bigram_debug > 1) {
if (!w_prev->best_choices.singleton()) {
w_prev->PrintBestChoices();
}
if (!w->best_choices.singleton()) {
w->PrintBestChoices();
}
}
float best_rating = 0.0;
int best_idx = 0;
WERD_CHOICE_IT prev_it(&w_prev->best_choices);
for (prev_it.mark_cycle_pt(); !prev_it.cycled_list(); prev_it.forward()) {
WERD_CHOICE *p1 = prev_it.data();
WERD_CHOICE strip1(w->uch_set);
{
int p1start, p1end;
p1->GetNonSuperscriptSpan(&p1start, &p1end);
strip1 = p1->shallow_copy(p1start, p1end);
}
WERD_CHOICE_IT w_it(&w->best_choices);
for (w_it.mark_cycle_pt(); !w_it.cycled_list(); w_it.forward()) {
WERD_CHOICE *p2 = w_it.data();
WERD_CHOICE strip2(w->uch_set);
{
int p2start, p2end;
p2->GetNonSuperscriptSpan(&p2start, &p2end);
strip2 = p2->shallow_copy(p2start, p2end);
}
if (w->tesseract->getDict().valid_bigram(strip1, strip2)) {
overrides_word1.push_back(p1);
overrides_word2.push_back(p2);
if (overrides_word1.size() == 1 ||
p1->rating() + p2->rating() < best_rating) {
best_rating = p1->rating() + p2->rating();
best_idx = overrides_word1.size() - 1;
}
}
}
}
if (overrides_word1.size() >= 1) {
// Excellent, we have some bigram matches.
if (EqualIgnoringCaseAndTerminalPunct(*w_prev->best_choice,
*overrides_word1[best_idx]) &&
EqualIgnoringCaseAndTerminalPunct(*w->best_choice,
*overrides_word2[best_idx])) {
if (tessedit_bigram_debug > 1) {
tprintf("Top choice \"%s %s\" verified (sans case) by bigram "
"model.\n", orig_w1_str.string(), orig_w2_str.string());
}
continue;
}
STRING new_w1_str = overrides_word1[best_idx]->unichar_string();
STRING new_w2_str = overrides_word2[best_idx]->unichar_string();
if (new_w1_str != orig_w1_str) {
w_prev->ReplaceBestChoice(overrides_word1[best_idx]);
}
if (new_w2_str != orig_w2_str) {
w->ReplaceBestChoice(overrides_word2[best_idx]);
}
if (tessedit_bigram_debug > 0) {
STRING choices_description;
int num_bigram_choices
= overrides_word1.size() * overrides_word2.size();
if (num_bigram_choices == 1) {
choices_description = "This was the unique bigram choice.";
} else {
if (tessedit_bigram_debug > 1) {
STRING bigrams_list;
const int kMaxChoicesToPrint = 20;
for (int i = 0; i < overrides_word1.size() &&
i < kMaxChoicesToPrint; i++) {
if (i > 0) { bigrams_list += ", "; }
WERD_CHOICE *p1 = overrides_word1[i];
WERD_CHOICE *p2 = overrides_word2[i];
bigrams_list += p1->unichar_string() + " " + p2->unichar_string();
if (i == kMaxChoicesToPrint) {
bigrams_list += " ...";
}
}
choices_description = "There were many choices: {";
choices_description += bigrams_list;
choices_description += "}";
} else {
choices_description.add_str_int("There were ", num_bigram_choices);
choices_description += " compatible bigrams.";
}
}
tprintf("Replaced \"%s %s\" with \"%s %s\" with bigram model. %s\n",
orig_w1_str.string(), orig_w2_str.string(),
new_w1_str.string(), new_w2_str.string(),
choices_description.string());
}
}
}
}
void Tesseract::rejection_passes(PAGE_RES* page_res,
ETEXT_DESC* monitor,
const TBOX* target_word_box,
const char* word_config) {
PAGE_RES_IT page_res_it(page_res);
// ****************** Pass 5 *******************
// Gather statistics on rejects.
int word_index = 0;
while (!tessedit_test_adaption && page_res_it.word() != NULL) {
set_global_loc_code(LOC_MM_ADAPT);
WERD_RES* word = page_res_it.word();
word_index++;
if (monitor != NULL) {
monitor->ocr_alive = TRUE;
monitor->progress = 95 + 5 * word_index / stats_.word_count;
}
if (word->rebuild_word == NULL) {
// Word was not processed by tesseract.
page_res_it.forward();
continue;
}
check_debug_pt(word, 70);
// changed by jetsoft
// specific to its needs to extract one word when need
if (target_word_box &&
!ProcessTargetWord(word->word->bounding_box(),
*target_word_box, word_config, 4)) {
page_res_it.forward();
continue;
}
// end jetsoft
page_res_it.rej_stat_word();
int chars_in_word = word->reject_map.length();
int rejects_in_word = word->reject_map.reject_count();
int blob_quality = word_blob_quality(word, page_res_it.row()->row);
stats_.doc_blob_quality += blob_quality;
int outline_errs = word_outline_errs(word);
stats_.doc_outline_errs += outline_errs;
inT16 all_char_quality;
inT16 accepted_all_char_quality;
word_char_quality(word, page_res_it.row()->row,
&all_char_quality, &accepted_all_char_quality);
stats_.doc_char_quality += all_char_quality;
uinT8 permuter_type = word->best_choice->permuter();
if ((permuter_type == SYSTEM_DAWG_PERM) ||
(permuter_type == FREQ_DAWG_PERM) ||
(permuter_type == USER_DAWG_PERM)) {
stats_.good_char_count += chars_in_word - rejects_in_word;
stats_.doc_good_char_quality += accepted_all_char_quality;
}
check_debug_pt(word, 80);
if (tessedit_reject_bad_qual_wds &&
(blob_quality == 0) && (outline_errs >= chars_in_word))
word->reject_map.rej_word_bad_quality();
check_debug_pt(word, 90);
page_res_it.forward();
}
if (tessedit_debug_quality_metrics) {
tprintf
("QUALITY: num_chs= %d num_rejs= %d %5.3f blob_qual= %d %5.3f"
" outline_errs= %d %5.3f char_qual= %d %5.3f good_ch_qual= %d %5.3f\n",
page_res->char_count, page_res->rej_count,
page_res->rej_count / static_cast<float>(page_res->char_count),
stats_.doc_blob_quality,
stats_.doc_blob_quality / static_cast<float>(page_res->char_count),
stats_.doc_outline_errs,
stats_.doc_outline_errs / static_cast<float>(page_res->char_count),
stats_.doc_char_quality,
stats_.doc_char_quality / static_cast<float>(page_res->char_count),
stats_.doc_good_char_quality,
(stats_.good_char_count > 0) ?
(stats_.doc_good_char_quality /
static_cast<float>(stats_.good_char_count)) : 0.0);
}
BOOL8 good_quality_doc =
((page_res->rej_count / static_cast<float>(page_res->char_count)) <=
quality_rej_pc) &&
(stats_.doc_blob_quality / static_cast<float>(page_res->char_count) >=
quality_blob_pc) &&
(stats_.doc_outline_errs / static_cast<float>(page_res->char_count) <=
quality_outline_pc) &&
(stats_.doc_char_quality / static_cast<float>(page_res->char_count) >=
quality_char_pc);
// ****************** Pass 6 *******************
// Do whole document or whole block rejection pass
if (!tessedit_test_adaption) {
set_global_loc_code(LOC_DOC_BLK_REJ);
quality_based_rejection(page_res_it, good_quality_doc);
}
}
void Tesseract::blamer_pass(PAGE_RES* page_res) {
if (!wordrec_run_blamer) return;
PAGE_RES_IT page_res_it(page_res);
for (page_res_it.restart_page(); page_res_it.word() != NULL;
page_res_it.forward()) {
WERD_RES *word = page_res_it.word();
BlamerBundle::LastChanceBlame(wordrec_debug_blamer, word);
page_res->blame_reasons[word->blamer_bundle->incorrect_result_reason()]++;
}
tprintf("Blame reasons:\n");
for (int bl = 0; bl < IRR_NUM_REASONS; ++bl) {
tprintf("%s %d\n", BlamerBundle::IncorrectReasonName(
static_cast<IncorrectResultReason>(bl)),
page_res->blame_reasons[bl]);
}
if (page_res->misadaption_log.length() > 0) {
tprintf("Misadaption log:\n");
for (int i = 0; i < page_res->misadaption_log.length(); ++i) {
tprintf("%s\n", page_res->misadaption_log[i].string());
}
}
}
// Sets script positions and detects smallcaps on all output words.
void Tesseract::script_pos_pass(PAGE_RES* page_res) {
PAGE_RES_IT page_res_it(page_res);
for (page_res_it.restart_page(); page_res_it.word() != NULL;
page_res_it.forward()) {
WERD_RES* word = page_res_it.word();
if (word->word->flag(W_REP_CHAR)) {
page_res_it.forward();
continue;
}
float x_height = page_res_it.block()->block->x_height();
float word_x_height = word->x_height;
if (word_x_height < word->best_choice->min_x_height() ||
word_x_height > word->best_choice->max_x_height()) {
word_x_height = (word->best_choice->min_x_height() +
word->best_choice->max_x_height()) / 2.0f;
}
// Test for small caps. Word capheight must be close to block xheight,
// and word must contain no lower case letters, and at least one upper case.
double small_cap_xheight = x_height * kXHeightCapRatio;
double small_cap_delta = (x_height - small_cap_xheight) / 2.0;
if (word->uch_set->script_has_xheight() &&
small_cap_xheight - small_cap_delta <= word_x_height &&
word_x_height <= small_cap_xheight + small_cap_delta) {
// Scan for upper/lower.
int num_upper = 0;
int num_lower = 0;
for (int i = 0; i < word->best_choice->length(); ++i) {
if (word->uch_set->get_isupper(word->best_choice->unichar_id(i)))
++num_upper;
else if (word->uch_set->get_islower(word->best_choice->unichar_id(i)))
++num_lower;
}
if (num_upper > 0 && num_lower == 0)
word->small_caps = true;
}
word->SetScriptPositions();
}
}
// Factored helper considers the indexed word and updates all the pointed
// values.
static void EvaluateWord(const PointerVector<WERD_RES>& words, int index,
float* rating, float* certainty, bool* bad,
bool* valid_permuter, int* right, int* next_left) {
*right = -MAX_INT32;
*next_left = MAX_INT32;
if (index < words.size()) {
WERD_CHOICE* choice = words[index]->best_choice;
if (choice == NULL) {
*bad = true;
} else {
*rating += choice->rating();
*certainty = MIN(*certainty, choice->certainty());
if (!Dict::valid_word_permuter(choice->permuter(), false))
*valid_permuter = false;
}
*right = words[index]->word->bounding_box().right();
if (index + 1 < words.size())
*next_left = words[index + 1]->word->bounding_box().left();
} else {
*valid_permuter = false;
*bad = true;
}
}
// Helper chooses the best combination of words, transferring good ones from
// new_words to best_words. To win, a new word must have (better rating and
// certainty) or (better permuter status and rating within rating ratio and
// certainty within certainty margin) than current best.
// All the new_words are consumed (moved to best_words or deleted.)
// The return value is the number of new_words used minus the number of
// best_words that remain in the output.
static int SelectBestWords(double rating_ratio,
double certainty_margin,
bool debug,
PointerVector<WERD_RES>* new_words,
PointerVector<WERD_RES>* best_words) {
// Process the smallest groups of words that have an overlapping word
// boundary at the end.
GenericVector<WERD_RES*> out_words;
// Index into each word vector (best, new).
int b = 0, n = 0;
int num_best = 0, num_new = 0;
while (b < best_words->size() || n < new_words->size()) {
// Start of the current run in each.
int start_b = b, start_n = n;
// Rating of the current run in each.
float b_rating = 0.0f, n_rating = 0.0f;
// Certainty of the current run in each.
float b_certainty = 0.0f, n_certainty = 0.0f;
// True if any word is missing its best choice.
bool b_bad = false, n_bad = false;
// True if all words have a valid permuter.
bool b_valid_permuter = true, n_valid_permuter = true;
while (b < best_words->size() || n < new_words->size()) {
int b_right = -MAX_INT32;
int next_b_left = MAX_INT32;
EvaluateWord(*best_words, b, &b_rating, &b_certainty, &b_bad,
&b_valid_permuter, &b_right, &next_b_left);
int n_right = -MAX_INT32;
int next_n_left = MAX_INT32;
EvaluateWord(*new_words, n, &n_rating, &n_certainty, &n_bad,
&n_valid_permuter, &n_right, &next_n_left);
if (MAX(b_right, n_right) < MIN(next_b_left, next_n_left)) {
// The word breaks overlap. [start_b,b] and [start_n, n] match.
break;
}
// Keep searching for the matching word break.
if ((b_right < n_right && b < best_words->size()) ||
n == new_words->size())
++b;
else
++n;
}
bool new_better = false;
if (!n_bad && (b_bad || (n_certainty > b_certainty &&
n_rating < b_rating) ||
(!b_valid_permuter && n_valid_permuter &&
n_rating < b_rating * rating_ratio &&
n_certainty > b_certainty - certainty_margin))) {
// New is better.
for (int i = start_n; i <= n; ++i) {
out_words.push_back((*new_words)[i]);
(*new_words)[i] = NULL;
++num_new;
}
new_better = true;
} else if (!b_bad) {
// Current best is better.
for (int i = start_b; i <= b; ++i) {
out_words.push_back((*best_words)[i]);
(*best_words)[i] = NULL;
++num_best;
}
}
int end_b = b < best_words->size() ? b + 1 : b;
int end_n = n < new_words->size() ? n + 1 : n;
if (debug) {
tprintf("%d new words %s than %d old words: r: %g v %g c: %g v %g"
" valid dict: %d v %d\n",
end_n - start_n, new_better ? "better" : "worse",
end_b - start_b, n_rating, b_rating,
n_certainty, b_certainty, n_valid_permuter, b_valid_permuter);
}
// Move on to the next group.
b = end_b;
n = end_n;
}
// Transfer from out_words to best_words.
best_words->clear();
for (int i = 0; i < out_words.size(); ++i)
best_words->push_back(out_words[i]);
return num_new - num_best;
}
// Helper to recognize the word using the given (language-specific) tesseract.
// Returns positive if this recognizer found more new best words than the
// number kept from best_words.
int Tesseract::RetryWithLanguage(const WordData& word_data,
WordRecognizer recognizer,
WERD_RES** in_word,
PointerVector<WERD_RES>* best_words) {
bool debug = classify_debug_level || cube_debug_level;
if (debug) {
tprintf("Trying word using lang %s, oem %d\n",
lang.string(), static_cast<int>(tessedit_ocr_engine_mode));
}
// Run the recognizer on the word.
PointerVector<WERD_RES> new_words;
(this->*recognizer)(word_data, in_word, &new_words);
if (new_words.empty()) {
// Transfer input word to new_words, as the classifier must have put
// the result back in the input.
new_words.push_back(*in_word);
*in_word = NULL;
}
if (debug) {
for (int i = 0; i < new_words.size(); ++i)
new_words[i]->DebugTopChoice("Lang result");
}
// Initial version is a bit of a hack based on better certainty and rating
// (to reduce false positives from cube) or a dictionary vs non-dictionary
// word.
return SelectBestWords(classify_max_rating_ratio,
classify_max_certainty_margin,
debug, &new_words, best_words);
}
// Helper returns true if all the words are acceptable.
static bool WordsAcceptable(const PointerVector<WERD_RES>& words) {
for (int w = 0; w < words.size(); ++w) {
if (words[w]->tess_failed || !words[w]->tess_accepted) return false;
}
return true;
}
// Moves good-looking "noise"/diacritics from the reject list to the main
// blob list on the current word. Returns true if anything was done, and
// sets make_next_word_fuzzy if blob(s) were added to the end of the word.
bool Tesseract::ReassignDiacritics(int pass, PAGE_RES_IT* pr_it,
bool* make_next_word_fuzzy) {
*make_next_word_fuzzy = false;
WERD* real_word = pr_it->word()->word;
if (real_word->rej_cblob_list()->empty() ||
real_word->cblob_list()->empty() ||
real_word->rej_cblob_list()->length() > noise_maxperword)
return false;
real_word->rej_cblob_list()->sort(&C_BLOB::SortByXMiddle);
// Get the noise outlines into a vector with matching bool map.
GenericVector<C_OUTLINE*> outlines;
real_word->GetNoiseOutlines(&outlines);
GenericVector<bool> word_wanted;
GenericVector<bool> overlapped_any_blob;
GenericVector<C_BLOB*> target_blobs;
AssignDiacriticsToOverlappingBlobs(outlines, pass, real_word, pr_it,
&word_wanted, &overlapped_any_blob,
&target_blobs);
// Filter the outlines that overlapped any blob and put them into the word
// now. This simplifies the remaining task and also makes it more accurate
// as it has more completed blobs to work on.
GenericVector<bool> wanted;
GenericVector<C_BLOB*> wanted_blobs;
GenericVector<C_OUTLINE*> wanted_outlines;
int num_overlapped = 0;
int num_overlapped_used = 0;
for (int i = 0; i < overlapped_any_blob.size(); ++i) {
if (overlapped_any_blob[i]) {
++num_overlapped;
if (word_wanted[i]) ++num_overlapped_used;
wanted.push_back(word_wanted[i]);
wanted_blobs.push_back(target_blobs[i]);
wanted_outlines.push_back(outlines[i]);
outlines[i] = NULL;
}
}
real_word->AddSelectedOutlines(wanted, wanted_blobs, wanted_outlines, NULL);
AssignDiacriticsToNewBlobs(outlines, pass, real_word, pr_it, &word_wanted,
&target_blobs);
int non_overlapped = 0;
int non_overlapped_used = 0;
for (int i = 0; i < word_wanted.size(); ++i) {
if (word_wanted[i]) ++non_overlapped_used;
if (outlines[i] != NULL) ++non_overlapped_used;
}
if (debug_noise_removal) {
tprintf("Used %d/%d overlapped %d/%d non-overlaped diacritics on word:",
num_overlapped_used, num_overlapped, non_overlapped_used,
non_overlapped);
real_word->bounding_box().print();
}
// Now we have decided which outlines we want, put them into the real_word.
if (real_word->AddSelectedOutlines(word_wanted, target_blobs, outlines,
make_next_word_fuzzy)) {
pr_it->MakeCurrentWordFuzzy();
}
// TODO(rays) Parts of combos have a deep copy of the real word, and need
// to have their noise outlines moved/assigned in the same way!!
return num_overlapped_used != 0 || non_overlapped_used != 0;
}
// Attempts to put noise/diacritic outlines into the blobs that they overlap.
// Input: a set of noisy outlines that probably belong to the real_word.
// Output: word_wanted indicates which outlines are to be assigned to a blob,
// target_blobs indicates which to assign to, and overlapped_any_blob is
// true for all outlines that overlapped a blob.
void Tesseract::AssignDiacriticsToOverlappingBlobs(
const GenericVector<C_OUTLINE*>& outlines, int pass, WERD* real_word,
PAGE_RES_IT* pr_it, GenericVector<bool>* word_wanted,
GenericVector<bool>* overlapped_any_blob,
GenericVector<C_BLOB*>* target_blobs) {
GenericVector<bool> blob_wanted;
word_wanted->init_to_size(outlines.size(), false);
overlapped_any_blob->init_to_size(outlines.size(), false);
target_blobs->init_to_size(outlines.size(), NULL);
// For each real blob, find the outlines that seriously overlap it.
// A single blob could be several merged characters, so there can be quite
// a few outlines overlapping, and the full engine needs to be used to chop
// and join to get a sensible result.
C_BLOB_IT blob_it(real_word->cblob_list());
for (blob_it.mark_cycle_pt(); !blob_it.cycled_list(); blob_it.forward()) {
C_BLOB* blob = blob_it.data();
TBOX blob_box = blob->bounding_box();
blob_wanted.init_to_size(outlines.size(), false);
int num_blob_outlines = 0;
for (int i = 0; i < outlines.size(); ++i) {
if (blob_box.major_x_overlap(outlines[i]->bounding_box()) &&
!(*word_wanted)[i]) {
blob_wanted[i] = true;
(*overlapped_any_blob)[i] = true;
++num_blob_outlines;
}
}
if (debug_noise_removal) {
tprintf("%d noise outlines overlap blob at:", num_blob_outlines);
blob_box.print();
}
// If any outlines overlap the blob, and not too many, classify the blob
// (using the full engine, languages and all), and choose the maximal
// combination of outlines that doesn't hurt the end-result classification
// by too much. Mark them as wanted.
if (0 < num_blob_outlines && num_blob_outlines < noise_maxperblob) {
if (SelectGoodDiacriticOutlines(pass, noise_cert_basechar, pr_it, blob,
outlines, num_blob_outlines,
&blob_wanted)) {
for (int i = 0; i < blob_wanted.size(); ++i) {
if (blob_wanted[i]) {
// Claim the outline and record where it is going.
(*word_wanted)[i] = true;
(*target_blobs)[i] = blob;
}
}
}
}
}
}
// Attempts to assign non-overlapping outlines to their nearest blobs or
// make new blobs out of them.
void Tesseract::AssignDiacriticsToNewBlobs(
const GenericVector<C_OUTLINE*>& outlines, int pass, WERD* real_word,
PAGE_RES_IT* pr_it, GenericVector<bool>* word_wanted,
GenericVector<C_BLOB*>* target_blobs) {
GenericVector<bool> blob_wanted;
word_wanted->init_to_size(outlines.size(), false);
target_blobs->init_to_size(outlines.size(), NULL);
// Check for outlines that need to be turned into stand-alone blobs.
for (int i = 0; i < outlines.size(); ++i) {
if (outlines[i] == NULL) continue;
// Get a set of adjacent outlines that don't overlap any existing blob.
blob_wanted.init_to_size(outlines.size(), false);
int num_blob_outlines = 0;
TBOX total_ol_box(outlines[i]->bounding_box());
while (i < outlines.size() && outlines[i] != NULL) {
blob_wanted[i] = true;
total_ol_box += outlines[i]->bounding_box();
++i;
++num_blob_outlines;
}
// Find the insertion point.
C_BLOB_IT blob_it(real_word->cblob_list());
while (!blob_it.at_last() &&
blob_it.data_relative(1)->bounding_box().left() <=
total_ol_box.left()) {
blob_it.forward();
}
// Choose which combination of them we actually want and where to put
// them.
if (debug_noise_removal)
tprintf("Num blobless outlines = %d\n", num_blob_outlines);
C_BLOB* left_blob = blob_it.data();
TBOX left_box = left_blob->bounding_box();
C_BLOB* right_blob = blob_it.at_last() ? NULL : blob_it.data_relative(1);
if ((left_box.x_overlap(total_ol_box) || right_blob == NULL ||
!right_blob->bounding_box().x_overlap(total_ol_box)) &&
SelectGoodDiacriticOutlines(pass, noise_cert_disjoint, pr_it, left_blob,
outlines, num_blob_outlines,
&blob_wanted)) {
if (debug_noise_removal) tprintf("Added to left blob\n");
for (int j = 0; j < blob_wanted.size(); ++j) {
if (blob_wanted[j]) {
(*word_wanted)[j] = true;
(*target_blobs)[j] = left_blob;
}
}
} else if (right_blob != NULL &&
(!left_box.x_overlap(total_ol_box) ||
right_blob->bounding_box().x_overlap(total_ol_box)) &&
SelectGoodDiacriticOutlines(pass, noise_cert_disjoint, pr_it,
right_blob, outlines,
num_blob_outlines, &blob_wanted)) {
if (debug_noise_removal) tprintf("Added to right blob\n");
for (int j = 0; j < blob_wanted.size(); ++j) {
if (blob_wanted[j]) {
(*word_wanted)[j] = true;
(*target_blobs)[j] = right_blob;
}
}
} else if (SelectGoodDiacriticOutlines(pass, noise_cert_punc, pr_it, NULL,
outlines, num_blob_outlines,
&blob_wanted)) {
if (debug_noise_removal) tprintf("Fitted between blobs\n");
for (int j = 0; j < blob_wanted.size(); ++j) {
if (blob_wanted[j]) {
(*word_wanted)[j] = true;
(*target_blobs)[j] = NULL;
}
}
}
}
}
// Starting with ok_outlines set to indicate which outlines overlap the blob,
// chooses the optimal set (approximately) and returns true if any outlines
// are desired, in which case ok_outlines indicates which ones.
bool Tesseract::SelectGoodDiacriticOutlines(
int pass, float certainty_threshold, PAGE_RES_IT* pr_it, C_BLOB* blob,
const GenericVector<C_OUTLINE*>& outlines, int num_outlines,
GenericVector<bool>* ok_outlines) {
STRING best_str;
float target_cert = certainty_threshold;
if (blob != NULL) {
float target_c2;
target_cert = ClassifyBlobAsWord(pass, pr_it, blob, &best_str, &target_c2);
if (debug_noise_removal) {
tprintf("No Noise blob classified as %s=%g(%g) at:", best_str.string(),
target_cert, target_c2);
blob->bounding_box().print();
}
target_cert -= (target_cert - certainty_threshold) * noise_cert_factor;
}
GenericVector<bool> test_outlines = *ok_outlines;
// Start with all the outlines in.
STRING all_str;
GenericVector<bool> best_outlines = *ok_outlines;
float best_cert = ClassifyBlobPlusOutlines(test_outlines, outlines, pass,
pr_it, blob, &all_str);
if (debug_noise_removal) {
TBOX ol_box;
for (int i = 0; i < test_outlines.size(); ++i) {
if (test_outlines[i]) ol_box += outlines[i]->bounding_box();
}
tprintf("All Noise blob classified as %s=%g, delta=%g at:",
all_str.string(), best_cert, best_cert - target_cert);
ol_box.print();
}
// Iteratively zero out the bit that improves the certainty the most, until
// we get past the threshold, have zero bits, or fail to improve.
int best_index = 0; // To zero out.
while (num_outlines > 1 && best_index >= 0 &&
(blob == NULL || best_cert < target_cert || blob != NULL)) {
// Find the best bit to zero out.
best_index = -1;
for (int i = 0; i < outlines.size(); ++i) {
if (test_outlines[i]) {
test_outlines[i] = false;
STRING str;
float cert = ClassifyBlobPlusOutlines(test_outlines, outlines, pass,
pr_it, blob, &str);
if (debug_noise_removal) {
TBOX ol_box;
for (int j = 0; j < outlines.size(); ++j) {
if (test_outlines[j]) ol_box += outlines[j]->bounding_box();
tprintf("%d", test_outlines[j]);
}
tprintf(" blob classified as %s=%g, delta=%g) at:", str.string(),
cert, cert - target_cert);
ol_box.print();
}
if (cert > best_cert) {
best_cert = cert;
best_index = i;
best_outlines = test_outlines;
}
test_outlines[i] = true;
}
}
if (best_index >= 0) {
test_outlines[best_index] = false;
--num_outlines;
}
}
if (best_cert >= target_cert) {
// Save the best combination.
*ok_outlines = best_outlines;
if (debug_noise_removal) {
tprintf("%s noise combination ", blob ? "Adding" : "New");
for (int i = 0; i < best_outlines.size(); ++i) {
tprintf("%d", best_outlines[i]);
}
tprintf(" yields certainty %g, beating target of %g\n", best_cert,
target_cert);
}
return true;
}
return false;
}
// Classifies the given blob plus the outlines flagged by ok_outlines, undoes
// the inclusion of the outlines, and returns the certainty of the raw choice.
float Tesseract::ClassifyBlobPlusOutlines(
const GenericVector<bool>& ok_outlines,
const GenericVector<C_OUTLINE*>& outlines, int pass_n, PAGE_RES_IT* pr_it,
C_BLOB* blob, STRING* best_str) {
C_OUTLINE_IT ol_it;
C_OUTLINE* first_to_keep = NULL;
if (blob != NULL) {
// Add the required outlines to the blob.
ol_it.set_to_list(blob->out_list());
first_to_keep = ol_it.data();
}
for (int i = 0; i < ok_outlines.size(); ++i) {
if (ok_outlines[i]) {
// This outline is to be added.
if (blob == NULL) {
blob = new C_BLOB(outlines[i]);
ol_it.set_to_list(blob->out_list());
} else {
ol_it.add_before_stay_put(outlines[i]);
}
}
}
float c2;
float cert = ClassifyBlobAsWord(pass_n, pr_it, blob, best_str, &c2);
ol_it.move_to_first();
if (first_to_keep == NULL) {
// We created blob. Empty its outlines and delete it.
for (; !ol_it.empty(); ol_it.forward()) ol_it.extract();
delete blob;
cert = -c2;
} else {
// Remove the outlines that we put in.
for (; ol_it.data() != first_to_keep; ol_it.forward()) {
ol_it.extract();
}
}
return cert;
}
// Classifies the given blob (part of word_data->word->word) as an individual
// word, using languages, chopper etc, returning only the certainty of the
// best raw choice, and undoing all the work done to fake out the word.
float Tesseract::ClassifyBlobAsWord(int pass_n, PAGE_RES_IT* pr_it,
C_BLOB* blob, STRING* best_str, float* c2) {
WERD* real_word = pr_it->word()->word;
WERD* word = real_word->ConstructFromSingleBlob(
real_word->flag(W_BOL), real_word->flag(W_EOL), C_BLOB::deep_copy(blob));
WERD_RES* word_res = pr_it->InsertSimpleCloneWord(*pr_it->word(), word);
// Get a new iterator that points to the new word.
PAGE_RES_IT it(pr_it->page_res);
while (it.word() != word_res && it.word() != NULL) it.forward();
ASSERT_HOST(it.word() == word_res);
WordData wd(it);
// Force full initialization.
SetupWordPassN(1, &wd);
classify_word_and_language(pass_n, &it, &wd);
if (debug_noise_removal) {
tprintf("word xheight=%g, row=%g, range=[%g,%g]\n", word_res->x_height,
wd.row->x_height(), wd.word->raw_choice->min_x_height(),
wd.word->raw_choice->max_x_height());
}
float cert = wd.word->raw_choice->certainty();
float rat = wd.word->raw_choice->rating();
*c2 = rat > 0.0f ? cert * cert / rat : 0.0f;
*best_str = wd.word->raw_choice->unichar_string();
it.DeleteCurrentWord();
pr_it->ResetWordIterator();
return cert;
}
// Generic function for classifying a word. Can be used either for pass1 or
// pass2 according to the function passed to recognizer.
// word_data holds the word to be recognized, and its block and row, and
// pr_it points to the word as well, in case we are running LSTM and it wants
// to output multiple words.
// Recognizes in the current language, and if successful that is all.
// If recognition was not successful, tries all available languages until
// it gets a successful result or runs out of languages. Keeps the best result.
void Tesseract::classify_word_and_language(int pass_n, PAGE_RES_IT* pr_it,
WordData* word_data) {
WordRecognizer recognizer = pass_n == 1 ? &Tesseract::classify_word_pass1
: &Tesseract::classify_word_pass2;
// Best result so far.
PointerVector<WERD_RES> best_words;
// Points to the best result. May be word or in lang_words.
WERD_RES* word = word_data->word;
clock_t start_t = clock();
if (classify_debug_level || cube_debug_level) {
tprintf("%s word with lang %s at:",
word->done ? "Already done" : "Processing",
most_recently_used_->lang.string());
word->word->bounding_box().print();
}
if (word->done) {
// If done on pass1, leave it as-is.
if (!word->tess_failed)
most_recently_used_ = word->tesseract;
return;
}
int sub = sub_langs_.size();
if (most_recently_used_ != this) {
// Get the index of the most_recently_used_.
for (sub = 0; sub < sub_langs_.size() &&
most_recently_used_ != sub_langs_[sub]; ++sub) {}
}
most_recently_used_->RetryWithLanguage(
*word_data, recognizer, &word_data->lang_words[sub], &best_words);
Tesseract* best_lang_tess = most_recently_used_;
if (!WordsAcceptable(best_words)) {
// Try all the other languages to see if they are any better.
if (most_recently_used_ != this &&
this->RetryWithLanguage(*word_data, recognizer,
&word_data->lang_words[sub_langs_.size()],
&best_words) > 0) {
best_lang_tess = this;
}
for (int i = 0; !WordsAcceptable(best_words) && i < sub_langs_.size();
++i) {
if (most_recently_used_ != sub_langs_[i] &&
sub_langs_[i]->RetryWithLanguage(*word_data, recognizer,
&word_data->lang_words[i],
&best_words) > 0) {
best_lang_tess = sub_langs_[i];
}
}
}
most_recently_used_ = best_lang_tess;
if (!best_words.empty()) {
if (best_words.size() == 1 && !best_words[0]->combination) {
// Move the best single result to the main word.
word_data->word->ConsumeWordResults(best_words[0]);
} else {
// Words came from LSTM, and must be moved to the PAGE_RES properly.
word_data->word = best_words.back();
pr_it->ReplaceCurrentWord(&best_words);
}
ASSERT_HOST(word_data->word->box_word != NULL);
} else {
tprintf("no best words!!\n");
}
clock_t ocr_t = clock();
if (tessedit_timing_debug) {
tprintf("%s (ocr took %.2f sec)\n",
word->best_choice->unichar_string().string(),
static_cast<double>(ocr_t-start_t)/CLOCKS_PER_SEC);
}
}
/**
* classify_word_pass1
*
* Baseline normalize the word and pass it to Tess.
*/
void Tesseract::classify_word_pass1(const WordData& word_data,
WERD_RES** in_word,
PointerVector<WERD_RES>* out_words) {
ROW* row = word_data.row;
BLOCK* block = word_data.block;
prev_word_best_choice_ = word_data.prev_word != NULL
? word_data.prev_word->word->best_choice : NULL;
#ifndef ANDROID_BUILD
// If we only intend to run cube - run it and return.
if (tessedit_ocr_engine_mode == OEM_CUBE_ONLY) {
cube_word_pass1(block, row, *in_word);
return;
}
#endif
WERD_RES* word = *in_word;
match_word_pass_n(1, word, row, block);
if (!word->tess_failed && !word->word->flag(W_REP_CHAR)) {
word->tess_would_adapt = AdaptableWord(word);
bool adapt_ok = word_adaptable(word, tessedit_tess_adaption_mode);
if (adapt_ok) {
// Send word to adaptive classifier for training.
word->BestChoiceToCorrectText();
LearnWord(NULL, word);
// Mark misadaptions if running blamer.
if (word->blamer_bundle != NULL) {
word->blamer_bundle->SetMisAdaptionDebug(word->best_choice,
wordrec_debug_blamer);
}
}
if (tessedit_enable_doc_dict && !word->IsAmbiguous())
tess_add_doc_word(word->best_choice);
}
}
// Helper to report the result of the xheight fix.
void Tesseract::ReportXhtFixResult(bool accept_new_word, float new_x_ht,
WERD_RES* word, WERD_RES* new_word) {
tprintf("New XHT Match:%s = %s ",
word->best_choice->unichar_string().string(),
word->best_choice->debug_string().string());
word->reject_map.print(debug_fp);
tprintf(" -> %s = %s ",
new_word->best_choice->unichar_string().string(),
new_word->best_choice->debug_string().string());
new_word->reject_map.print(debug_fp);
tprintf(" %s->%s %s %s\n",
word->guessed_x_ht ? "GUESS" : "CERT",
new_word->guessed_x_ht ? "GUESS" : "CERT",
new_x_ht > 0.1 ? "STILL DOUBT" : "OK",
accept_new_word ? "ACCEPTED" : "");
}
// Run the x-height fix-up, based on min/max top/bottom information in
// unicharset.
// Returns true if the word was changed.
// See the comment in fixxht.cpp for a description of the overall process.
bool Tesseract::TrainedXheightFix(WERD_RES *word, BLOCK* block, ROW *row) {
bool accept_new_x_ht = false;
int original_misfits = CountMisfitTops(word);
if (original_misfits == 0)
return false;
float baseline_shift = 0.0f;
float new_x_ht = ComputeCompatibleXheight(word, &baseline_shift);
if (baseline_shift != 0.0f) {
// Try the shift on its own first.
if (!TestNewNormalization(original_misfits, baseline_shift, word->x_height,
word, block, row))
return false;
original_misfits = CountMisfitTops(word);
if (original_misfits > 0) {
float new_baseline_shift;
// Now recompute the new x_height.
new_x_ht = ComputeCompatibleXheight(word, &new_baseline_shift);
if (new_x_ht >= kMinRefitXHeightFraction * word->x_height) {
// No test of return value here, as we are definitely making a change
// to the word by shifting the baseline.
TestNewNormalization(original_misfits, baseline_shift, new_x_ht,
word, block, row);
}
}
return true;
} else if (new_x_ht >= kMinRefitXHeightFraction * word->x_height) {
return TestNewNormalization(original_misfits, 0.0f, new_x_ht,
word, block, row);
} else {
return false;
}
}
// Runs recognition with the test baseline shift and x-height and returns true
// if there was an improvement in recognition result.
bool Tesseract::TestNewNormalization(int original_misfits,
float baseline_shift, float new_x_ht,
WERD_RES *word, BLOCK* block, ROW *row) {
bool accept_new_x_ht = false;
WERD_RES new_x_ht_word(word->word);
if (word->blamer_bundle != NULL) {
new_x_ht_word.blamer_bundle = new BlamerBundle();
new_x_ht_word.blamer_bundle->CopyTruth(*(word->blamer_bundle));
}
new_x_ht_word.x_height = new_x_ht;
new_x_ht_word.baseline_shift = baseline_shift;
new_x_ht_word.caps_height = 0.0;
new_x_ht_word.SetupForRecognition(
unicharset, this, BestPix(), tessedit_ocr_engine_mode, NULL,
classify_bln_numeric_mode, textord_use_cjk_fp_model,
poly_allow_detailed_fx, row, block);
match_word_pass_n(2, &new_x_ht_word, row, block);
if (!new_x_ht_word.tess_failed) {
int new_misfits = CountMisfitTops(&new_x_ht_word);
if (debug_x_ht_level >= 1) {
tprintf("Old misfits=%d with x-height %f, new=%d with x-height %f\n",
original_misfits, word->x_height,
new_misfits, new_x_ht);
tprintf("Old rating= %f, certainty=%f, new=%f, %f\n",
word->best_choice->rating(), word->best_choice->certainty(),
new_x_ht_word.best_choice->rating(),
new_x_ht_word.best_choice->certainty());
}
// The misfits must improve and either the rating or certainty.
accept_new_x_ht = new_misfits < original_misfits &&
(new_x_ht_word.best_choice->certainty() >
word->best_choice->certainty() ||
new_x_ht_word.best_choice->rating() <
word->best_choice->rating());
if (debug_x_ht_level >= 1) {
ReportXhtFixResult(accept_new_x_ht, new_x_ht, word, &new_x_ht_word);
}
}
if (accept_new_x_ht) {
word->ConsumeWordResults(&new_x_ht_word);
return true;
}
return false;
}
/**
* classify_word_pass2
*
* Control what to do with the word in pass 2
*/
void Tesseract::classify_word_pass2(const WordData& word_data,
WERD_RES** in_word,
PointerVector<WERD_RES>* out_words) {
// Return if we do not want to run Tesseract.
if (tessedit_ocr_engine_mode != OEM_TESSERACT_ONLY &&
tessedit_ocr_engine_mode != OEM_TESSERACT_CUBE_COMBINED &&
word_data.word->best_choice != NULL)
return;
if (tessedit_ocr_engine_mode == OEM_CUBE_ONLY) {
return;
}
ROW* row = word_data.row;
BLOCK* block = word_data.block;
WERD_RES* word = *in_word;
prev_word_best_choice_ = word_data.prev_word != NULL
? word_data.prev_word->word->best_choice : NULL;
set_global_subloc_code(SUBLOC_NORM);
check_debug_pt(word, 30);
if (!word->done) {
word->caps_height = 0.0;
if (word->x_height == 0.0f)
word->x_height = row->x_height();
match_word_pass_n(2, word, row, block);
check_debug_pt(word, 40);
}
SubAndSuperscriptFix(word);
if (!word->tess_failed && !word->word->flag(W_REP_CHAR)) {
if (unicharset.top_bottom_useful() && unicharset.script_has_xheight() &&
block->classify_rotation().y() == 0.0f) {
// Use the tops and bottoms since they are available.
TrainedXheightFix(word, block, row);
}
set_global_subloc_code(SUBLOC_NORM);
}
#ifndef GRAPHICS_DISABLED
if (tessedit_display_outwords) {
if (fx_win == NULL)
create_fx_win();
clear_fx_win();
word->rebuild_word->plot(fx_win);
TBOX wbox = word->rebuild_word->bounding_box();
fx_win->ZoomToRectangle(wbox.left(), wbox.top(),
wbox.right(), wbox.bottom());
ScrollView::Update();
}
#endif
set_global_subloc_code(SUBLOC_NORM);
check_debug_pt(word, 50);
}
/**
* match_word_pass2
*
* Baseline normalize the word and pass it to Tess.
*/
void Tesseract::match_word_pass_n(int pass_n, WERD_RES *word,
ROW *row, BLOCK* block) {
if (word->tess_failed) return;
tess_segment_pass_n(pass_n, word);
if (!word->tess_failed) {
if (!word->word->flag (W_REP_CHAR)) {
word->fix_quotes();
if (tessedit_fix_hyphens)
word->fix_hyphens();
/* Dont trust fix_quotes! - though I think I've fixed the bug */
if (word->best_choice->length() != word->box_word->length()) {
tprintf("POST FIX_QUOTES FAIL String:\"%s\"; Strlen=%d;"
" #Blobs=%d\n",
word->best_choice->debug_string().string(),
word->best_choice->length(),
word->box_word->length());
}
word->tess_accepted = tess_acceptable_word(word);
// Also sets word->done flag
make_reject_map(word, row, pass_n);
}
}
set_word_fonts(word);
ASSERT_HOST(word->raw_choice != NULL);
}
// Helper to return the best rated BLOB_CHOICE in the whole word that matches
// the given char_id, or NULL if none can be found.
static BLOB_CHOICE* FindBestMatchingChoice(UNICHAR_ID char_id,
WERD_RES* word_res) {
// Find the corresponding best BLOB_CHOICE from any position in the word_res.
BLOB_CHOICE* best_choice = NULL;
for (int i = 0; i < word_res->best_choice->length(); ++i) {
BLOB_CHOICE* choice = FindMatchingChoice(char_id,
word_res->GetBlobChoices(i));
if (choice != NULL) {
if (best_choice == NULL || choice->rating() < best_choice->rating())
best_choice = choice;
}
}
return best_choice;
}
// Helper to insert blob_choice in each location in the leader word if there is
// no matching BLOB_CHOICE there already, and correct any incorrect results
// in the best_choice.
static void CorrectRepcharChoices(BLOB_CHOICE* blob_choice,
WERD_RES* word_res) {
WERD_CHOICE* word = word_res->best_choice;
for (int i = 0; i < word_res->best_choice->length(); ++i) {
BLOB_CHOICE* choice = FindMatchingChoice(blob_choice->unichar_id(),
word_res->GetBlobChoices(i));
if (choice == NULL) {
BLOB_CHOICE_IT choice_it(word_res->GetBlobChoices(i));
choice_it.add_before_stay_put(new BLOB_CHOICE(*blob_choice));
}
}
// Correct any incorrect results in word.
for (int i = 0; i < word->length(); ++i) {
if (word->unichar_id(i) != blob_choice->unichar_id())
word->set_unichar_id(blob_choice->unichar_id(), i);
}
}
/**
* fix_rep_char()
* The word is a repeated char. (Leader.) Find the repeated char character.
* Create the appropriate single-word or multi-word sequence according to
* the size of spaces in between blobs, and correct the classifications
* where some of the characters disagree with the majority.
*/
void Tesseract::fix_rep_char(PAGE_RES_IT* page_res_it) {
WERD_RES *word_res = page_res_it->word();
const WERD_CHOICE &word = *(word_res->best_choice);
// Find the frequency of each unique character in the word.
SortHelper<UNICHAR_ID> rep_ch(word.length());
for (int i = 0; i < word.length(); ++i) {
rep_ch.Add(word.unichar_id(i), 1);
}
// Find the most frequent result.
UNICHAR_ID maxch_id = INVALID_UNICHAR_ID; // most common char
int max_count = rep_ch.MaxCount(&maxch_id);
// Find the best exemplar of a classifier result for maxch_id.
BLOB_CHOICE* best_choice = FindBestMatchingChoice(maxch_id, word_res);
if (best_choice == NULL) {
tprintf("Failed to find a choice for %s, occurring %d times\n",
word_res->uch_set->debug_str(maxch_id).string(), max_count);
return;
}
word_res->done = TRUE;
// Measure the mean space.
int gap_count = 0;
WERD* werd = word_res->word;
C_BLOB_IT blob_it(werd->cblob_list());
C_BLOB* prev_blob = blob_it.data();
for (blob_it.forward(); !blob_it.at_first(); blob_it.forward()) {
C_BLOB* blob = blob_it.data();
int gap = blob->bounding_box().left();
gap -= prev_blob->bounding_box().right();
++gap_count;
prev_blob = blob;
}
// Just correct existing classification.
CorrectRepcharChoices(best_choice, word_res);
word_res->reject_map.initialise(word.length());
}
ACCEPTABLE_WERD_TYPE Tesseract::acceptable_word_string(
const UNICHARSET& char_set, const char *s, const char *lengths) {
int i = 0;
int offset = 0;
int leading_punct_count;
int upper_count = 0;
int hyphen_pos = -1;
ACCEPTABLE_WERD_TYPE word_type = AC_UNACCEPTABLE;
if (strlen (lengths) > 20)
return word_type;
/* Single Leading punctuation char*/
if (s[offset] != '\0' && STRING(chs_leading_punct).contains(s[offset]))
offset += lengths[i++];
leading_punct_count = i;
/* Initial cap */
while (s[offset] != '\0' && char_set.get_isupper(s + offset, lengths[i])) {
offset += lengths[i++];
upper_count++;
}
if (upper_count > 1) {
word_type = AC_UPPER_CASE;
} else {
/* Lower case word, possibly with an initial cap */
while (s[offset] != '\0' && char_set.get_islower(s + offset, lengths[i])) {
offset += lengths[i++];
}
if (i - leading_punct_count < quality_min_initial_alphas_reqd)
goto not_a_word;
/*
Allow a single hyphen in a lower case word
- dont trust upper case - I've seen several cases of "H" -> "I-I"
*/
if (lengths[i] == 1 && s[offset] == '-') {
hyphen_pos = i;
offset += lengths[i++];
if (s[offset] != '\0') {
while ((s[offset] != '\0') &&
char_set.get_islower(s + offset, lengths[i])) {
offset += lengths[i++];
}
if (i < hyphen_pos + 3)
goto not_a_word;
}
} else {
/* Allow "'s" in NON hyphenated lower case words */
if (lengths[i] == 1 && (s[offset] == '\'') &&
lengths[i + 1] == 1 && (s[offset + lengths[i]] == 's')) {
offset += lengths[i++];
offset += lengths[i++];
}
}
if (upper_count > 0)
word_type = AC_INITIAL_CAP;
else
word_type = AC_LOWER_CASE;
}
/* Up to two different, constrained trailing punctuation chars */
if (lengths[i] == 1 && s[offset] != '\0' &&
STRING(chs_trailing_punct1).contains(s[offset]))
offset += lengths[i++];
if (lengths[i] == 1 && s[offset] != '\0' && i > 0 &&
s[offset - lengths[i - 1]] != s[offset] &&
STRING(chs_trailing_punct2).contains (s[offset]))
offset += lengths[i++];
if (s[offset] != '\0')
word_type = AC_UNACCEPTABLE;
not_a_word:
if (word_type == AC_UNACCEPTABLE) {
/* Look for abbreviation string */
i = 0;
offset = 0;
if (s[0] != '\0' && char_set.get_isupper(s, lengths[0])) {
word_type = AC_UC_ABBREV;
while (s[offset] != '\0' &&
char_set.get_isupper(s + offset, lengths[i]) &&
lengths[i + 1] == 1 && s[offset + lengths[i]] == '.') {
offset += lengths[i++];
offset += lengths[i++];
}
}
else if (s[0] != '\0' && char_set.get_islower(s, lengths[0])) {
word_type = AC_LC_ABBREV;
while (s[offset] != '\0' &&
char_set.get_islower(s + offset, lengths[i]) &&
lengths[i + 1] == 1 && s[offset + lengths[i]] == '.') {
offset += lengths[i++];
offset += lengths[i++];
}
}
if (s[offset] != '\0')
word_type = AC_UNACCEPTABLE;
}
return word_type;
}
BOOL8 Tesseract::check_debug_pt(WERD_RES *word, int location) {
BOOL8 show_map_detail = FALSE;
inT16 i;
if (!test_pt)
return FALSE;
tessedit_rejection_debug.set_value (FALSE);
debug_x_ht_level.set_value(0);
if (word->word->bounding_box ().contains (FCOORD (test_pt_x, test_pt_y))) {
if (location < 0)
return TRUE; // For breakpoint use
tessedit_rejection_debug.set_value (TRUE);
debug_x_ht_level.set_value(2);
tprintf ("\n\nTESTWD::");
switch (location) {
case 0:
tprintf ("classify_word_pass1 start\n");
word->word->print();
break;
case 10:
tprintf ("make_reject_map: initial map");
break;
case 20:
tprintf ("make_reject_map: after NN");
break;
case 30:
tprintf ("classify_word_pass2 - START");
break;
case 40:
tprintf ("classify_word_pass2 - Pre Xht");
break;
case 50:
tprintf ("classify_word_pass2 - END");
show_map_detail = TRUE;
break;
case 60:
tprintf ("fixspace");
break;
case 70:
tprintf ("MM pass START");
break;
case 80:
tprintf ("MM pass END");
break;
case 90:
tprintf ("After Poor quality rejection");
break;
case 100:
tprintf ("unrej_good_quality_words - START");
break;
case 110:
tprintf ("unrej_good_quality_words - END");
break;
case 120:
tprintf ("Write results pass");
show_map_detail = TRUE;
break;
}
if (word->best_choice != NULL) {
tprintf(" \"%s\" ", word->best_choice->unichar_string().string());
word->reject_map.print(debug_fp);
tprintf("\n");
if (show_map_detail) {
tprintf("\"%s\"\n", word->best_choice->unichar_string().string());
for (i = 0; word->best_choice->unichar_string()[i] != '\0'; i++) {
tprintf("**** \"%c\" ****\n", word->best_choice->unichar_string()[i]);
word->reject_map[i].full_print(debug_fp);
}
}
} else {
tprintf("null best choice\n");
}
tprintf ("Tess Accepted: %s\n", word->tess_accepted ? "TRUE" : "FALSE");
tprintf ("Done flag: %s\n\n", word->done ? "TRUE" : "FALSE");
return TRUE;
} else {
return FALSE;
}
}
/**
* find_modal_font
*
* Find the modal font and remove from the stats.
*/
static void find_modal_font( //good chars in word
STATS *fonts, //font stats
inT16 *font_out, //output font
inT8 *font_count //output count
) {
inT16 font; //font index
inT32 count; //pile couat
if (fonts->get_total () > 0) {
font = (inT16) fonts->mode ();
*font_out = font;
count = fonts->pile_count (font);
*font_count = count < MAX_INT8 ? count : MAX_INT8;
fonts->add (font, -*font_count);
}
else {
*font_out = -1;
*font_count = 0;
}
}
/**
* set_word_fonts
*
* Get the fonts for the word.
*/
void Tesseract::set_word_fonts(WERD_RES *word) {
// Don't try to set the word fonts for a cube word, as the configs
// will be meaningless.
if (word->chopped_word == NULL) return;
ASSERT_HOST(word->best_choice != NULL);
int fontinfo_size = get_fontinfo_table().size();
if (fontinfo_size == 0) return;
GenericVector<int> font_total_score;
font_total_score.init_to_size(fontinfo_size, 0);
word->italic = 0;
word->bold = 0;
// Compute the font scores for the word
if (tessedit_debug_fonts) {
tprintf("Examining fonts in %s\n",
word->best_choice->debug_string().string());
}
for (int b = 0; b < word->best_choice->length(); ++b) {
BLOB_CHOICE* choice = word->GetBlobChoice(b);
if (choice == NULL) continue;
const GenericVector<ScoredFont>& fonts = choice->fonts();
for (int f = 0; f < fonts.size(); ++f) {
int fontinfo_id = fonts[f].fontinfo_id;
if (0 <= fontinfo_id && fontinfo_id < fontinfo_size) {
font_total_score[fontinfo_id] += fonts[f].score;
}
}
}
// Find the top and 2nd choice for the word.
int score1 = 0, score2 = 0;
inT16 font_id1 = -1, font_id2 = -1;
for (int f = 0; f < fontinfo_size; ++f) {
if (tessedit_debug_fonts && font_total_score[f] > 0) {
tprintf("Font %s, total score = %d\n",
fontinfo_table_.get(f).name, font_total_score[f]);
}
if (font_total_score[f] > score1) {
score2 = score1;
font_id2 = font_id1;
score1 = font_total_score[f];
font_id1 = f;
} else if (font_total_score[f] > score2) {
score2 = font_total_score[f];
font_id2 = f;
}
}
word->fontinfo = font_id1 >= 0 ? &fontinfo_table_.get(font_id1) : NULL;
word->fontinfo2 = font_id2 >= 0 ? &fontinfo_table_.get(font_id2) : NULL;
// Each score has a limit of MAX_UINT16, so divide by that to get the number
// of "votes" for that font, ie number of perfect scores.
word->fontinfo_id_count = ClipToRange(score1 / MAX_UINT16, 1, MAX_INT8);
word->fontinfo_id2_count = ClipToRange(score2 / MAX_UINT16, 0, MAX_INT8);
if (score1 > 0) {
FontInfo fi = fontinfo_table_.get(font_id1);
if (tessedit_debug_fonts) {
if (word->fontinfo_id2_count > 0) {
tprintf("Word modal font=%s, score=%d, 2nd choice %s/%d\n",
fi.name, word->fontinfo_id_count,
fontinfo_table_.get(font_id2).name,
word->fontinfo_id2_count);
} else {
tprintf("Word modal font=%s, score=%d. No 2nd choice\n",
fi.name, word->fontinfo_id_count);
}
}
word->italic = (fi.is_italic() ? 1 : -1) * word->fontinfo_id_count;
word->bold = (fi.is_bold() ? 1 : -1) * word->fontinfo_id_count;
}
}
/**
* font_recognition_pass
*
* Smooth the fonts for the document.
*/
void Tesseract::font_recognition_pass(PAGE_RES* page_res) {
PAGE_RES_IT page_res_it(page_res);
WERD_RES *word; // current word
STATS doc_fonts(0, font_table_size_); // font counters
// Gather font id statistics.
for (page_res_it.restart_page(); page_res_it.word() != NULL;
page_res_it.forward()) {
word = page_res_it.word();
if (word->fontinfo != NULL) {
doc_fonts.add(word->fontinfo->universal_id, word->fontinfo_id_count);
}
if (word->fontinfo2 != NULL) {
doc_fonts.add(word->fontinfo2->universal_id, word->fontinfo_id2_count);
}
}
inT16 doc_font; // modal font
inT8 doc_font_count; // modal font
find_modal_font(&doc_fonts, &doc_font, &doc_font_count);
if (doc_font_count == 0)
return;
// Get the modal font pointer.
const FontInfo* modal_font = NULL;
for (page_res_it.restart_page(); page_res_it.word() != NULL;
page_res_it.forward()) {
word = page_res_it.word();
if (word->fontinfo != NULL && word->fontinfo->universal_id == doc_font) {
modal_font = word->fontinfo;
break;
}
if (word->fontinfo2 != NULL && word->fontinfo2->universal_id == doc_font) {
modal_font = word->fontinfo2;
break;
}
}
ASSERT_HOST(modal_font != NULL);
// Assign modal font to weak words.
for (page_res_it.restart_page(); page_res_it.word() != NULL;
page_res_it.forward()) {
word = page_res_it.word();
int length = word->best_choice->length();
int count = word->fontinfo_id_count;
if (!(count == length || (length > 3 && count >= length * 3 / 4))) {
word->fontinfo = modal_font;
// Counts only get 1 as it came from the doc.
word->fontinfo_id_count = 1;
word->italic = modal_font->is_italic() ? 1 : -1;
word->bold = modal_font->is_bold() ? 1 : -1;
}
}
}
// If a word has multiple alternates check if the best choice is in the
// dictionary. If not, replace it with an alternate that exists in the
// dictionary.
void Tesseract::dictionary_correction_pass(PAGE_RES *page_res) {
PAGE_RES_IT word_it(page_res);
for (WERD_RES* word = word_it.word(); word != NULL;
word = word_it.forward()) {
if (word->best_choices.singleton())
continue; // There are no alternates.
WERD_CHOICE* best = word->best_choice;
if (word->tesseract->getDict().valid_word(*best) != 0)
continue; // The best choice is in the dictionary.
WERD_CHOICE_IT choice_it(&word->best_choices);
for (choice_it.mark_cycle_pt(); !choice_it.cycled_list();
choice_it.forward()) {
WERD_CHOICE* alternate = choice_it.data();
if (word->tesseract->getDict().valid_word(*alternate)) {
// The alternate choice is in the dictionary.
if (tessedit_bigram_debug) {
tprintf("Dictionary correction replaces best choice '%s' with '%s'\n",
best->unichar_string().string(),
alternate->unichar_string().string());
}
// Replace the 'best' choice with a better choice.
word->ReplaceBestChoice(alternate);
break;
}
}
}
}
} // namespace tesseract
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