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tesseract/ccmain/fixspace.cpp

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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 "mfcpch.h"
#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 "secname.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 = new WERD_RES(*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)) {
classify_word_and_language(&Tesseract::classify_word_pass2,
block, row, word);
}
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 += ' ';
}
}
}
#ifndef SECURE_NAMES
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");
}
#endif
}
}
/**
* @name uniformly_spaced()
* Return true if one of the following are true:
* - All inter-char gaps are the same width
* - The largest gap is no larger than twice the mean/median of the others
* - The largest gap is < normalised_max_nonspace
* **** REMEMBER - WE'RE NOW WORKING WITH A BLN WERD !!!
*/
BOOL8 Tesseract::uniformly_spaced(WERD_RES *word) {
TBOX box;
inT16 prev_right = -MAX_INT16;
inT16 gap;
inT16 max_gap = -MAX_INT16;
inT16 max_gap_count = 0;
STATS gap_stats(0, MAXSPACING);
BOOL8 result;
const ROW *row = word->denorm.row();
float max_non_space;
float normalised_max_nonspace;
inT16 i = 0;
inT16 offset = 0;
STRING punct_chars = "\"`',.:;";
for (TBLOB* blob = word->rebuild_word->blobs; blob != NULL;
blob = blob->next) {
box = blob->bounding_box();
if ((prev_right > -MAX_INT16) &&
(!punct_chars.contains(
word->best_choice->unichar_string()
[offset - word->best_choice->unichar_lengths()[i - 1]]) &&
!punct_chars.contains(
word->best_choice->unichar_string()[offset]))) {
gap = box.left() - prev_right;
if (gap < max_gap) {
gap_stats.add(gap, 1);
} else if (gap == max_gap) {
max_gap_count++;
} else {
if (max_gap_count > 0)
gap_stats.add(max_gap, max_gap_count);
max_gap = gap;
max_gap_count = 1;
}
}
prev_right = box.right();
offset += word->best_choice->unichar_lengths()[i++];
}
max_non_space = (row->space() + 3 * row->kern()) / 4;
normalised_max_nonspace = max_non_space * kBlnXHeight / row->x_height();
result = (
gap_stats.get_total() == 0 ||
max_gap <= normalised_max_nonspace ||
(gap_stats.get_total() > 2 && max_gap <= 2 * gap_stats.median()) ||
(gap_stats.get_total() <= 2 && max_gap <= 2 * gap_stats.mean()));
#ifndef SECURE_NAMES
if ((debug_fix_space_level > 1)) {
if (result) {
tprintf(
"ACCEPT SPACING FOR: \"%s\" norm_maxnon = %f max=%d maxcount=%d "
"total=%d mean=%f median=%f\n",
word->best_choice->unichar_string().string(), normalised_max_nonspace,
max_gap, max_gap_count, gap_stats.get_total(), gap_stats.mean(),
gap_stats.median());
} else {
tprintf(
"REJECT SPACING FOR: \"%s\" norm_maxnon = %f max=%d maxcount=%d "
"total=%d mean=%f median=%f\n",
word->best_choice->unichar_string().string(), normalised_max_nonspace,
max_gap, max_gap_count, gap_stats.get_total(), gap_stats.mean(),
gap_stats.median());
}
}
#endif
return result;
}
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;
WERD_RES *new_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);
new_word_res = new WERD_RES;
old_word_res = best_perm_it.data();
old_word_res->combination = TRUE; // Kludge to force deep copy
*new_word_res = *old_word_res; // deep copy
old_word_res->combination = FALSE; // Undo kludge
current_perm_it.add_to_end(new_word_res);
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.
TBLOB* blob = word_res->rebuild_word->blobs;
// 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 && blob != NULL; i++, blob = blob->next) {
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) {
TBLOB* blob = word->rebuild_word->blobs;
UNICHAR_ID space = word->uch_set->unichar_to_id(" ");
for (i = 0; i < word->best_choice->length() && blob != NULL;
++i, blob = blob->next) {
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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