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tesseract/ccmain/fixspace.cpp
Ray Smith 0e868ef377 Major change to improve layout analysis for heavily diacritic languages: 
Tha, Vie, Kan, Tel etc.
There is a new overlap detector that detects when diacritics
cause a big increase in textline overlap. In such cases, diacritics from
overlap regions are kept separate from layout analysis completely, allowing
textline formation to happen without them. The diacritics are then assigned
to 0, 1 or 2 close words at the end of layout analysis, using and modifying
an old noise detection data path.
The stored diacritics are used or not during recognition according to the
character classifier's liking for them.
2015-05-12 16:47:02 -07:00

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/******************************************************************
* File: fixspace.cpp (Formerly fixspace.c)
* Description: Implements a pass over the page res, exploring the alternative
* spacing possibilities, trying to use context to improve the
* word spacing
* Author: Phil Cheatle
* Created: Thu Oct 21 11:38:43 BST 1993
*
* (C) Copyright 1993, 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 <ctype.h>
#include "reject.h"
#include "statistc.h"
#include "control.h"
#include "fixspace.h"
#include "genblob.h"
#include "tessvars.h"
#include "tessbox.h"
#include "globals.h"
#include "tesseractclass.h"
#define PERFECT_WERDS 999
#define MAXSPACING 128 /*max expected spacing in pix */
namespace tesseract {
/**
* @name fix_fuzzy_spaces()
* Walk over the page finding sequences of words joined by fuzzy spaces. Extract
* them as a sublist, process the sublist to find the optimal arrangement of
* spaces then replace the sublist in the ROW_RES.
*
* @param monitor progress monitor
* @param word_count count of words in doc
* @param[out] page_res
*/
void Tesseract::fix_fuzzy_spaces(ETEXT_DESC *monitor,
inT32 word_count,
PAGE_RES *page_res) {
BLOCK_RES_IT block_res_it;
ROW_RES_IT row_res_it;
WERD_RES_IT word_res_it_from;
WERD_RES_IT word_res_it_to;
WERD_RES *word_res;
WERD_RES_LIST fuzzy_space_words;
inT16 new_length;
BOOL8 prevent_null_wd_fixsp; // DONT process blobless wds
inT32 word_index; // current word
block_res_it.set_to_list(&page_res->block_res_list);
word_index = 0;
for (block_res_it.mark_cycle_pt(); !block_res_it.cycled_list();
block_res_it.forward()) {
row_res_it.set_to_list(&block_res_it.data()->row_res_list);
for (row_res_it.mark_cycle_pt(); !row_res_it.cycled_list();
row_res_it.forward()) {
word_res_it_from.set_to_list(&row_res_it.data()->word_res_list);
while (!word_res_it_from.at_last()) {
word_res = word_res_it_from.data();
while (!word_res_it_from.at_last() &&
!(word_res->combination ||
word_res_it_from.data_relative(1)->word->flag(W_FUZZY_NON) ||
word_res_it_from.data_relative(1)->word->flag(W_FUZZY_SP))) {
fix_sp_fp_word(word_res_it_from, row_res_it.data()->row,
block_res_it.data()->block);
word_res = word_res_it_from.forward();
word_index++;
if (monitor != NULL) {
monitor->ocr_alive = TRUE;
monitor->progress = 90 + 5 * word_index / word_count;
if (monitor->deadline_exceeded() ||
(monitor->cancel != NULL &&
(*monitor->cancel)(monitor->cancel_this, stats_.dict_words)))
return;
}
}
if (!word_res_it_from.at_last()) {
word_res_it_to = word_res_it_from;
prevent_null_wd_fixsp =
word_res->word->cblob_list()->empty();
if (check_debug_pt(word_res, 60))
debug_fix_space_level.set_value(10);
word_res_it_to.forward();
word_index++;
if (monitor != NULL) {
monitor->ocr_alive = TRUE;
monitor->progress = 90 + 5 * word_index / word_count;
if (monitor->deadline_exceeded() ||
(monitor->cancel != NULL &&
(*monitor->cancel)(monitor->cancel_this, stats_.dict_words)))
return;
}
while (!word_res_it_to.at_last () &&
(word_res_it_to.data_relative(1)->word->flag(W_FUZZY_NON) ||
word_res_it_to.data_relative(1)->word->flag(W_FUZZY_SP))) {
if (check_debug_pt(word_res, 60))
debug_fix_space_level.set_value(10);
if (word_res->word->cblob_list()->empty())
prevent_null_wd_fixsp = TRUE;
word_res = word_res_it_to.forward();
}
if (check_debug_pt(word_res, 60))
debug_fix_space_level.set_value(10);
if (word_res->word->cblob_list()->empty())
prevent_null_wd_fixsp = TRUE;
if (prevent_null_wd_fixsp) {
word_res_it_from = word_res_it_to;
} else {
fuzzy_space_words.assign_to_sublist(&word_res_it_from,
&word_res_it_to);
fix_fuzzy_space_list(fuzzy_space_words,
row_res_it.data()->row,
block_res_it.data()->block);
new_length = fuzzy_space_words.length();
word_res_it_from.add_list_before(&fuzzy_space_words);
for (;
!word_res_it_from.at_last() && new_length > 0;
new_length--) {
word_res_it_from.forward();
}
}
if (test_pt)
debug_fix_space_level.set_value(0);
}
fix_sp_fp_word(word_res_it_from, row_res_it.data()->row,
block_res_it.data()->block);
// Last word in row
}
}
}
}
void Tesseract::fix_fuzzy_space_list(WERD_RES_LIST &best_perm,
ROW *row,
BLOCK* block) {
inT16 best_score;
WERD_RES_LIST current_perm;
inT16 current_score;
BOOL8 improved = FALSE;
best_score = eval_word_spacing(best_perm); // default score
dump_words(best_perm, best_score, 1, improved);
if (best_score != PERFECT_WERDS)
initialise_search(best_perm, current_perm);
while ((best_score != PERFECT_WERDS) && !current_perm.empty()) {
match_current_words(current_perm, row, block);
current_score = eval_word_spacing(current_perm);
dump_words(current_perm, current_score, 2, improved);
if (current_score > best_score) {
best_perm.clear();
best_perm.deep_copy(&current_perm, &WERD_RES::deep_copy);
best_score = current_score;
improved = TRUE;
}
if (current_score < PERFECT_WERDS)
transform_to_next_perm(current_perm);
}
dump_words(best_perm, best_score, 3, improved);
}
} // namespace tesseract
void initialise_search(WERD_RES_LIST &src_list, WERD_RES_LIST &new_list) {
WERD_RES_IT src_it(&src_list);
WERD_RES_IT new_it(&new_list);
WERD_RES *src_wd;
WERD_RES *new_wd;
for (src_it.mark_cycle_pt(); !src_it.cycled_list(); src_it.forward()) {
src_wd = src_it.data();
if (!src_wd->combination) {
new_wd = WERD_RES::deep_copy(src_wd);
new_wd->combination = FALSE;
new_wd->part_of_combo = FALSE;
new_it.add_after_then_move(new_wd);
}
}
}
namespace tesseract {
void Tesseract::match_current_words(WERD_RES_LIST &words, ROW *row,
BLOCK* block) {
WERD_RES_IT word_it(&words);
WERD_RES *word;
// Since we are not using PAGE_RES to iterate over words, we need to update
// prev_word_best_choice_ before calling classify_word_pass2().
prev_word_best_choice_ = NULL;
for (word_it.mark_cycle_pt(); !word_it.cycled_list(); word_it.forward()) {
word = word_it.data();
if ((!word->part_of_combo) && (word->box_word == NULL)) {
WordData word_data(block, row, word);
SetupWordPassN(2, &word_data);
classify_word_and_language(2, NULL, &word_data);
}
prev_word_best_choice_ = word->best_choice;
}
}
/**
* @name eval_word_spacing()
* The basic measure is the number of characters in contextually confirmed
* words. (I.e the word is done)
* If all words are contextually confirmed the evaluation is deemed perfect.
*
* Some fiddles are done to handle "1"s as these are VERY frequent causes of
* fuzzy spaces. The problem with the basic measure is that "561 63" would score
* the same as "56163", though given our knowledge that the space is fuzzy, and
* that there is a "1" next to the fuzzy space, we need to ensure that "56163"
* is prefered.
*
* The solution is to NOT COUNT the score of any word which has a digit at one
* end and a "1Il" as the character the other side of the space.
*
* Conversly, any character next to a "1" within a word is counted as a positive
* score. Thus "561 63" would score 4 (3 chars in a numeric word plus 1 side of
* the "1" joined). "56163" would score 7 - all chars in a numeric word + 2
* sides of a "1" joined.
*
* The joined 1 rule is applied to any word REGARDLESS of contextual
* confirmation. Thus "PS7a71 3/7a" scores 1 (neither word is contexutally
* confirmed. The only score is from the joined 1. "PS7a713/7a" scores 2.
*
*/
inT16 Tesseract::eval_word_spacing(WERD_RES_LIST &word_res_list) {
WERD_RES_IT word_res_it(&word_res_list);
inT16 total_score = 0;
inT16 word_count = 0;
inT16 done_word_count = 0;
inT16 word_len;
inT16 i;
inT16 offset;
WERD_RES *word; // current word
inT16 prev_word_score = 0;
BOOL8 prev_word_done = FALSE;
BOOL8 prev_char_1 = FALSE; // prev ch a "1/I/l"?
BOOL8 prev_char_digit = FALSE; // prev ch 2..9 or 0
BOOL8 current_char_1 = FALSE;
BOOL8 current_word_ok_so_far;
STRING punct_chars = "!\"`',.:;";
BOOL8 prev_char_punct = FALSE;
BOOL8 current_char_punct = FALSE;
BOOL8 word_done = FALSE;
do {
word = word_res_it.data();
word_done = fixspace_thinks_word_done(word);
word_count++;
if (word->tess_failed) {
total_score += prev_word_score;
if (prev_word_done)
done_word_count++;
prev_word_score = 0;
prev_char_1 = FALSE;
prev_char_digit = FALSE;
prev_word_done = FALSE;
} else {
/*
Can we add the prev word score and potentially count this word?
Yes IF it didnt end in a 1 when the first char of this word is a digit
AND it didnt end in a digit when the first char of this word is a 1
*/
word_len = word->reject_map.length();
current_word_ok_so_far = FALSE;
if (!((prev_char_1 && digit_or_numeric_punct(word, 0)) ||
(prev_char_digit && (
(word_done &&
word->best_choice->unichar_lengths().string()[0] == 1 &&
word->best_choice->unichar_string()[0] == '1') ||
(!word_done && STRING(conflict_set_I_l_1).contains(
word->best_choice->unichar_string()[0])))))) {
total_score += prev_word_score;
if (prev_word_done)
done_word_count++;
current_word_ok_so_far = word_done;
}
if (current_word_ok_so_far) {
prev_word_done = TRUE;
prev_word_score = word_len;
} else {
prev_word_done = FALSE;
prev_word_score = 0;
}
/* Add 1 to total score for every joined 1 regardless of context and
rejtn */
for (i = 0, prev_char_1 = FALSE; i < word_len; i++) {
current_char_1 = word->best_choice->unichar_string()[i] == '1';
if (prev_char_1 || (current_char_1 && (i > 0)))
total_score++;
prev_char_1 = current_char_1;
}
/* Add 1 to total score for every joined punctuation regardless of context
and rejtn */
if (tessedit_prefer_joined_punct) {
for (i = 0, offset = 0, prev_char_punct = FALSE; i < word_len;
offset += word->best_choice->unichar_lengths()[i++]) {
current_char_punct =
punct_chars.contains(word->best_choice->unichar_string()[offset]);
if (prev_char_punct || (current_char_punct && i > 0))
total_score++;
prev_char_punct = current_char_punct;
}
}
prev_char_digit = digit_or_numeric_punct(word, word_len - 1);
for (i = 0, offset = 0; i < word_len - 1;
offset += word->best_choice->unichar_lengths()[i++]);
prev_char_1 =
((word_done && (word->best_choice->unichar_string()[offset] == '1'))
|| (!word_done && STRING(conflict_set_I_l_1).contains(
word->best_choice->unichar_string()[offset])));
}
/* Find next word */
do {
word_res_it.forward();
} while (word_res_it.data()->part_of_combo);
} while (!word_res_it.at_first());
total_score += prev_word_score;
if (prev_word_done)
done_word_count++;
if (done_word_count == word_count)
return PERFECT_WERDS;
else
return total_score;
}
BOOL8 Tesseract::digit_or_numeric_punct(WERD_RES *word, int char_position) {
int i;
int offset;
for (i = 0, offset = 0; i < char_position;
offset += word->best_choice->unichar_lengths()[i++]);
return (
word->uch_set->get_isdigit(
word->best_choice->unichar_string().string() + offset,
word->best_choice->unichar_lengths()[i]) ||
(word->best_choice->permuter() == NUMBER_PERM &&
STRING(numeric_punctuation).contains(
word->best_choice->unichar_string().string()[offset])));
}
} // namespace tesseract
/**
* @name transform_to_next_perm()
* Examines the current word list to find the smallest word gap size. Then walks
* the word list closing any gaps of this size by either inserted new
* combination words, or extending existing ones.
*
* The routine COULD be limited to stop it building words longer than N blobs.
*
* If there are no more gaps then it DELETES the entire list and returns the
* empty list to cause termination.
*/
void transform_to_next_perm(WERD_RES_LIST &words) {
WERD_RES_IT word_it(&words);
WERD_RES_IT prev_word_it(&words);
WERD_RES *word;
WERD_RES *prev_word;
WERD_RES *combo;
WERD *copy_word;
inT16 prev_right = -MAX_INT16;
TBOX box;
inT16 gap;
inT16 min_gap = MAX_INT16;
for (word_it.mark_cycle_pt(); !word_it.cycled_list(); word_it.forward()) {
word = word_it.data();
if (!word->part_of_combo) {
box = word->word->bounding_box();
if (prev_right > -MAX_INT16) {
gap = box.left() - prev_right;
if (gap < min_gap)
min_gap = gap;
}
prev_right = box.right();
}
}
if (min_gap < MAX_INT16) {
prev_right = -MAX_INT16; // back to start
word_it.set_to_list(&words);
// Note: we can't use cycle_pt due to inserted combos at start of list.
for (; (prev_right == -MAX_INT16) || !word_it.at_first();
word_it.forward()) {
word = word_it.data();
if (!word->part_of_combo) {
box = word->word->bounding_box();
if (prev_right > -MAX_INT16) {
gap = box.left() - prev_right;
if (gap <= min_gap) {
prev_word = prev_word_it.data();
if (prev_word->combination) {
combo = prev_word;
} else {
/* Make a new combination and insert before
* the first word being joined. */
copy_word = new WERD;
*copy_word = *(prev_word->word);
// deep copy
combo = new WERD_RES(copy_word);
combo->combination = TRUE;
combo->x_height = prev_word->x_height;
prev_word->part_of_combo = TRUE;
prev_word_it.add_before_then_move(combo);
}
combo->word->set_flag(W_EOL, word->word->flag(W_EOL));
if (word->combination) {
combo->word->join_on(word->word);
// Move blobs to combo
// old combo no longer needed
delete word_it.extract();
} else {
// Copy current wd to combo
combo->copy_on(word);
word->part_of_combo = TRUE;
}
combo->done = FALSE;
combo->ClearResults();
} else {
prev_word_it = word_it; // catch up
}
}
prev_right = box.right();
}
}
} else {
words.clear(); // signal termination
}
}
namespace tesseract {
void Tesseract::dump_words(WERD_RES_LIST &perm, inT16 score,
inT16 mode, BOOL8 improved) {
WERD_RES_IT word_res_it(&perm);
if (debug_fix_space_level > 0) {
if (mode == 1) {
stats_.dump_words_str = "";
for (word_res_it.mark_cycle_pt(); !word_res_it.cycled_list();
word_res_it.forward()) {
if (!word_res_it.data()->part_of_combo) {
stats_.dump_words_str +=
word_res_it.data()->best_choice->unichar_string();
stats_.dump_words_str += ' ';
}
}
}
if (debug_fix_space_level > 1) {
switch (mode) {
case 1:
tprintf("EXTRACTED (%d): \"", score);
break;
case 2:
tprintf("TESTED (%d): \"", score);
break;
case 3:
tprintf("RETURNED (%d): \"", score);
break;
}
for (word_res_it.mark_cycle_pt(); !word_res_it.cycled_list();
word_res_it.forward()) {
if (!word_res_it.data()->part_of_combo) {
tprintf("%s/%1d ",
word_res_it.data()->best_choice->unichar_string().string(),
(int)word_res_it.data()->best_choice->permuter());
}
}
tprintf("\"\n");
} else if (improved) {
tprintf("FIX SPACING \"%s\" => \"", stats_.dump_words_str.string());
for (word_res_it.mark_cycle_pt(); !word_res_it.cycled_list();
word_res_it.forward()) {
if (!word_res_it.data()->part_of_combo) {
tprintf("%s/%1d ",
word_res_it.data()->best_choice->unichar_string().string(),
(int)word_res_it.data()->best_choice->permuter());
}
}
tprintf("\"\n");
}
}
}
BOOL8 Tesseract::fixspace_thinks_word_done(WERD_RES *word) {
if (word->done)
return TRUE;
/*
Use all the standard pass 2 conditions for mode 5 in set_done() in
reject.c BUT DONT REJECT IF THE WERD IS AMBIGUOUS - FOR SPACING WE DONT
CARE WHETHER WE HAVE of/at on/an etc.
*/
if (fixsp_done_mode > 0 &&
(word->tess_accepted ||
(fixsp_done_mode == 2 && word->reject_map.reject_count() == 0) ||
fixsp_done_mode == 3) &&
(strchr(word->best_choice->unichar_string().string(), ' ') == NULL) &&
((word->best_choice->permuter() == SYSTEM_DAWG_PERM) ||
(word->best_choice->permuter() == FREQ_DAWG_PERM) ||
(word->best_choice->permuter() == USER_DAWG_PERM) ||
(word->best_choice->permuter() == NUMBER_PERM))) {
return TRUE;
} else {
return FALSE;
}
}
/**
* @name fix_sp_fp_word()
* Test the current word to see if it can be split by deleting noise blobs. If
* so, do the business.
* Return with the iterator pointing to the same place if the word is unchanged,
* or the last of the replacement words.
*/
void Tesseract::fix_sp_fp_word(WERD_RES_IT &word_res_it, ROW *row,
BLOCK* block) {
WERD_RES *word_res;
WERD_RES_LIST sub_word_list;
WERD_RES_IT sub_word_list_it(&sub_word_list);
inT16 blob_index;
inT16 new_length;
float junk;
word_res = word_res_it.data();
if (word_res->word->flag(W_REP_CHAR) ||
word_res->combination ||
word_res->part_of_combo ||
!word_res->word->flag(W_DONT_CHOP))
return;
blob_index = worst_noise_blob(word_res, &junk);
if (blob_index < 0)
return;
if (debug_fix_space_level > 1) {
tprintf("FP fixspace working on \"%s\"\n",
word_res->best_choice->unichar_string().string());
}
word_res->word->rej_cblob_list()->sort(c_blob_comparator);
sub_word_list_it.add_after_stay_put(word_res_it.extract());
fix_noisy_space_list(sub_word_list, row, block);
new_length = sub_word_list.length();
word_res_it.add_list_before(&sub_word_list);
for (; !word_res_it.at_last() && new_length > 1; new_length--) {
word_res_it.forward();
}
}
void Tesseract::fix_noisy_space_list(WERD_RES_LIST &best_perm, ROW *row,
BLOCK* block) {
inT16 best_score;
WERD_RES_IT best_perm_it(&best_perm);
WERD_RES_LIST current_perm;
WERD_RES_IT current_perm_it(&current_perm);
WERD_RES *old_word_res;
inT16 current_score;
BOOL8 improved = FALSE;
best_score = fp_eval_word_spacing(best_perm); // default score
dump_words(best_perm, best_score, 1, improved);
old_word_res = best_perm_it.data();
// Even deep_copy doesn't copy the underlying WERD unless its combination
// flag is true!.
old_word_res->combination = TRUE; // Kludge to force deep copy
current_perm_it.add_to_end(WERD_RES::deep_copy(old_word_res));
old_word_res->combination = FALSE; // Undo kludge
break_noisiest_blob_word(current_perm);
while (best_score != PERFECT_WERDS && !current_perm.empty()) {
match_current_words(current_perm, row, block);
current_score = fp_eval_word_spacing(current_perm);
dump_words(current_perm, current_score, 2, improved);
if (current_score > best_score) {
best_perm.clear();
best_perm.deep_copy(&current_perm, &WERD_RES::deep_copy);
best_score = current_score;
improved = TRUE;
}
if (current_score < PERFECT_WERDS) {
break_noisiest_blob_word(current_perm);
}
}
dump_words(best_perm, best_score, 3, improved);
}
/**
* break_noisiest_blob_word()
* Find the word with the blob which looks like the worst noise.
* Break the word into two, deleting the noise blob.
*/
void Tesseract::break_noisiest_blob_word(WERD_RES_LIST &words) {
WERD_RES_IT word_it(&words);
WERD_RES_IT worst_word_it;
float worst_noise_score = 9999;
int worst_blob_index = -1; // Noisiest blob of noisiest wd
int blob_index; // of wds noisiest blob
float noise_score; // of wds noisiest blob
WERD_RES *word_res;
C_BLOB_IT blob_it;
C_BLOB_IT rej_cblob_it;
C_BLOB_LIST new_blob_list;
C_BLOB_IT new_blob_it;
C_BLOB_IT new_rej_cblob_it;
WERD *new_word;
inT16 start_of_noise_blob;
inT16 i;
for (word_it.mark_cycle_pt(); !word_it.cycled_list(); word_it.forward()) {
blob_index = worst_noise_blob(word_it.data(), &noise_score);
if (blob_index > -1 && worst_noise_score > noise_score) {
worst_noise_score = noise_score;
worst_blob_index = blob_index;
worst_word_it = word_it;
}
}
if (worst_blob_index < 0) {
words.clear(); // signal termination
return;
}
/* Now split the worst_word_it */
word_res = worst_word_it.data();
/* Move blobs before noise blob to a new bloblist */
new_blob_it.set_to_list(&new_blob_list);
blob_it.set_to_list(word_res->word->cblob_list());
for (i = 0; i < worst_blob_index; i++, blob_it.forward()) {
new_blob_it.add_after_then_move(blob_it.extract());
}
start_of_noise_blob = blob_it.data()->bounding_box().left();
delete blob_it.extract(); // throw out noise blob
new_word = new WERD(&new_blob_list, word_res->word);
new_word->set_flag(W_EOL, FALSE);
word_res->word->set_flag(W_BOL, FALSE);
word_res->word->set_blanks(1); // After break
new_rej_cblob_it.set_to_list(new_word->rej_cblob_list());
rej_cblob_it.set_to_list(word_res->word->rej_cblob_list());
for (;
(!rej_cblob_it.empty() &&
(rej_cblob_it.data()->bounding_box().left() < start_of_noise_blob));
rej_cblob_it.forward()) {
new_rej_cblob_it.add_after_then_move(rej_cblob_it.extract());
}
WERD_RES* new_word_res = new WERD_RES(new_word);
new_word_res->combination = TRUE;
worst_word_it.add_before_then_move(new_word_res);
word_res->ClearResults();
}
inT16 Tesseract::worst_noise_blob(WERD_RES *word_res,
float *worst_noise_score) {
float noise_score[512];
int i;
int min_noise_blob; // 1st contender
int max_noise_blob; // last contender
int non_noise_count;
int worst_noise_blob; // Worst blob
float small_limit = kBlnXHeight * fixsp_small_outlines_size;
float non_noise_limit = kBlnXHeight * 0.8;
if (word_res->rebuild_word == NULL)
return -1; // Can't handle cube words.
// Normalised.
int blob_count = word_res->box_word->length();
ASSERT_HOST(blob_count <= 512);
if (blob_count < 5)
return -1; // too short to split
/* Get the noise scores for all blobs */
#ifndef SECURE_NAMES
if (debug_fix_space_level > 5)
tprintf("FP fixspace Noise metrics for \"%s\": ",
word_res->best_choice->unichar_string().string());
#endif
for (i = 0; i < blob_count && i < word_res->rebuild_word->NumBlobs(); i++) {
TBLOB* blob = word_res->rebuild_word->blobs[i];
if (word_res->reject_map[i].accepted())
noise_score[i] = non_noise_limit;
else
noise_score[i] = blob_noise_score(blob);
if (debug_fix_space_level > 5)
tprintf("%1.1f ", noise_score[i]);
}
if (debug_fix_space_level > 5)
tprintf("\n");
/* Now find the worst one which is far enough away from the end of the word */
non_noise_count = 0;
for (i = 0; i < blob_count && non_noise_count < fixsp_non_noise_limit; i++) {
if (noise_score[i] >= non_noise_limit) {
non_noise_count++;
}
}
if (non_noise_count < fixsp_non_noise_limit)
return -1;
min_noise_blob = i;
non_noise_count = 0;
for (i = blob_count - 1; i >= 0 && non_noise_count < fixsp_non_noise_limit;
i--) {
if (noise_score[i] >= non_noise_limit) {
non_noise_count++;
}
}
if (non_noise_count < fixsp_non_noise_limit)
return -1;
max_noise_blob = i;
if (min_noise_blob > max_noise_blob)
return -1;
*worst_noise_score = small_limit;
worst_noise_blob = -1;
for (i = min_noise_blob; i <= max_noise_blob; i++) {
if (noise_score[i] < *worst_noise_score) {
worst_noise_blob = i;
*worst_noise_score = noise_score[i];
}
}
return worst_noise_blob;
}
float Tesseract::blob_noise_score(TBLOB *blob) {
TBOX box; // BB of outline
inT16 outline_count = 0;
inT16 max_dimension;
inT16 largest_outline_dimension = 0;
for (TESSLINE* ol = blob->outlines; ol != NULL; ol= ol->next) {
outline_count++;
box = ol->bounding_box();
if (box.height() > box.width()) {
max_dimension = box.height();
} else {
max_dimension = box.width();
}
if (largest_outline_dimension < max_dimension)
largest_outline_dimension = max_dimension;
}
if (outline_count > 5) {
// penalise LOTS of blobs
largest_outline_dimension *= 2;
}
box = blob->bounding_box();
if (box.bottom() > kBlnBaselineOffset * 4 ||
box.top() < kBlnBaselineOffset / 2) {
// Lax blob is if high or low
largest_outline_dimension /= 2;
}
return largest_outline_dimension;
}
} // namespace tesseract
void fixspace_dbg(WERD_RES *word) {
TBOX box = word->word->bounding_box();
BOOL8 show_map_detail = FALSE;
inT16 i;
box.print();
tprintf(" \"%s\" ", word->best_choice->unichar_string().string());
tprintf("Blob count: %d (word); %d/%d (rebuild word)\n",
word->word->cblob_list()->length(),
word->rebuild_word->NumBlobs(),
word->box_word->length());
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);
}
}
tprintf("Tess Accepted: %s\n", word->tess_accepted ? "TRUE" : "FALSE");
tprintf("Done flag: %s\n\n", word->done ? "TRUE" : "FALSE");
}
/**
* fp_eval_word_spacing()
* Evaluation function for fixed pitch word lists.
*
* Basically, count the number of "nice" characters - those which are in tess
* acceptable words or in dict words and are not rejected.
* Penalise any potential noise chars
*/
namespace tesseract {
inT16 Tesseract::fp_eval_word_spacing(WERD_RES_LIST &word_res_list) {
WERD_RES_IT word_it(&word_res_list);
WERD_RES *word;
inT16 word_length;
inT16 score = 0;
inT16 i;
float small_limit = kBlnXHeight * fixsp_small_outlines_size;
for (word_it.mark_cycle_pt(); !word_it.cycled_list(); word_it.forward()) {
word = word_it.data();
if (word->rebuild_word == NULL)
continue; // Can't handle cube words.
word_length = word->reject_map.length();
if (word->done ||
word->tess_accepted ||
word->best_choice->permuter() == SYSTEM_DAWG_PERM ||
word->best_choice->permuter() == FREQ_DAWG_PERM ||
word->best_choice->permuter() == USER_DAWG_PERM ||
safe_dict_word(word) > 0) {
int num_blobs = word->rebuild_word->NumBlobs();
UNICHAR_ID space = word->uch_set->unichar_to_id(" ");
for (i = 0; i < word->best_choice->length() && i < num_blobs; ++i) {
TBLOB* blob = word->rebuild_word->blobs[i];
if (word->best_choice->unichar_id(i) == space ||
blob_noise_score(blob) < small_limit) {
score -= 1; // penalise possibly erroneous non-space
} else if (word->reject_map[i].accepted()) {
score++;
}
}
}
}
if (score < 0)
score = 0;
return score;
}
} // namespace tesseract
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