mirror/tesseract
1
0
Fork 0
mirror of https://github.com/tesseract-ocr/tesseract.git synced 2024-12-12 15:39:04 +08:00
Code Issues Actions 9 Packages Projects Releases Wiki Activity
57d669ff84
tesseract/wordrec/chopper.cpp
theraysmith b47efd2cc4 Changes to wordrec for 3.00 
git-svn-id: https://tesseract-ocr.googlecode.com/svn/trunk@304 d0cd1f9f-072b-0410-8dd7-cf729c803f20
2009-07-11 02:46:01 +00:00

889 lines
29 KiB
C++
Raw Blame History

/* -*-C-*-
********************************************************************************
*
* File: chopper.c (Formerly chopper.c)
* Description:
* Author: Mark Seaman, OCR Technology
* Created: Fri Oct 16 14:37:00 1987
* Modified: Tue Jul 30 16:18:52 1991 (Mark Seaman) marks@hpgrlt
* Language: C
* Package: N/A
* Status: Reusable Software Component
*
* (c) Copyright 1987, Hewlett-Packard Company.
** 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.
*
**************************************************************************/
/*----------------------------------------------------------------------
I n c l u d e s
----------------------------------------------------------------------*/
#include <math.h>
#include "chopper.h"
#include "assert.h"
#include "associate.h"
#include "callcpp.h"
#include "choices.h"
#include "const.h"
#include "findseam.h"
#include "freelist.h"
#include "globals.h"
#include "makechop.h"
#include "metrics.h"
#include "render.h"
#include "permute.h"
#include "pieces.h"
#include "seam.h"
#include "stopper.h"
#include "structures.h"
#include "tordvars.h"
#include "unicharset.h"
#include "wordclass.h"
#include "wordrec.h"
INT_VAR (repair_unchopped_blobs, 1, "Fix blobs that aren't chopped");
//?extern int tessedit_dangambigs_chop;
double_VAR(tessedit_certainty_threshold, -2.25, "Good blob limit");
BOOL_VAR(fragments_guide_chopper, FALSE,
"Use information from fragments to guide chopping process");
/*----------------------------------------------------------------------
M a c r o s
----------------------------------------------------------------------*/
/**********************************************************************
* bounds_inside
*
* Check to see if the bounding box of one thing is inside the
* bounding box of another.
**********************************************************************/
#define bounds_inside(inner_tl,inner_br,outer_tl,outer_br) \
((inner_tl.x >= outer_tl.x) && \
(inner_tl.y <= outer_tl.y) && \
(inner_br.x <= outer_br.x) && \
(inner_br.y >= outer_br.y)) \
/*----------------------------------------------------------------------
F u n c t i o n s
----------------------------------------------------------------------*/
/**********************************************************************
* preserve_outline_tree
*
* Copy the list of outlines.
**********************************************************************/
void preserve_outline(EDGEPT *start) {
EDGEPT *srcpt;
if (start == NULL)
return;
srcpt = start;
do {
srcpt->flags[1] = 1;
srcpt = srcpt->next;
}
while (srcpt != start);
srcpt->flags[1] = 2;
}
/**************************************************************************/
void preserve_outline_tree(TESSLINE *srcline) {
TESSLINE *outline;
for (outline = srcline; outline != NULL; outline = outline->next) {
preserve_outline (outline->loop);
}
if (srcline != NULL && srcline->child != NULL)
preserve_outline_tree (srcline->child);
}
/**********************************************************************
* restore_outline_tree
*
* Copy the list of outlines.
**********************************************************************/
EDGEPT *restore_outline(EDGEPT *start) {
EDGEPT *srcpt;
EDGEPT *real_start;
EDGEPT *deadpt;
if (start == NULL)
return NULL;
srcpt = start;
do {
if (srcpt->flags[1] == 2)
break;
srcpt = srcpt->next;
}
while (srcpt != start);
real_start = srcpt;
do {
if (srcpt->flags[1] == 0) {
deadpt = srcpt;
srcpt = srcpt->next;
srcpt->prev = deadpt->prev;
deadpt->prev->next = srcpt;
deadpt->prev->vec.x = srcpt->pos.x - deadpt->prev->pos.x;
deadpt->prev->vec.y = srcpt->pos.y - deadpt->prev->pos.y;
oldedgept(deadpt);
}
else
srcpt = srcpt->next;
}
while (srcpt != real_start);
return real_start;
}
/******************************************************************************/
void restore_outline_tree(TESSLINE *srcline) {
TESSLINE *outline;
for (outline = srcline; outline != NULL; outline = outline->next) {
outline->loop = restore_outline (outline->loop);
outline->start = outline->loop->pos;
}
if (srcline != NULL && srcline->child != NULL)
restore_outline_tree (srcline->child);
}
/**********************************************************************
* attempt_blob_chop
*
* Try to split the this blob after this one. Check to make sure that
* it was successful.
**********************************************************************/
SEAM *attempt_blob_chop(TWERD *word, inT32 blob_number, SEAMS seam_list) {
TBLOB *blob;
TBLOB *other_blob;
SEAM *seam;
TBLOB *last_blob;
TBLOB *next_blob;
inT16 x;
if (first_pass)
chops_attempted1++;
else
chops_attempted2++;
last_blob = NULL;
blob = word->blobs;
for (x = 0; x < blob_number; x++) {
last_blob = blob;
blob = blob->next;
}
next_blob = blob->next;
if (repair_unchopped_blobs)
preserve_outline_tree (blob->outlines);
other_blob = newblob (); /* Make new blob */
other_blob->next = blob->next;
other_blob->outlines = NULL;
blob->next = other_blob;
seam = pick_good_seam (blob);
if (chop_debug) {
if (seam != NULL) {
print_seam ("Good seam picked=", seam);
}
else
cprintf ("\n** no seam picked *** \n");
}
if (seam) {
apply_seam(blob, other_blob, seam);
}
if ((seam == NULL) ||
(blob->outlines == NULL) ||
(other_blob->outlines == NULL) ||
total_containment (blob, other_blob) ||
check_blob (other_blob) ||
!(check_seam_order (blob, seam) &&
check_seam_order (other_blob, seam)) ||
any_shared_split_points (seam_list, seam) ||
!test_insert_seam(seam_list, blob_number, blob, word->blobs)) {
blob->next = next_blob;
if (seam) {
undo_seam(blob, other_blob, seam);
delete_seam(seam);
#ifndef GRAPHICS_DISABLED
if (chop_debug) {
if (chop_debug >2)
display_blob(blob, Red);
cprintf ("\n** seam being removed ** \n");
}
#endif
}
else {
oldblob(other_blob);
}
if (repair_unchopped_blobs)
restore_outline_tree (blob->outlines);
return (NULL);
}
return (seam);
}
/**********************************************************************
* any_shared_split_points
*
* Return true if any of the splits share a point with this one.
**********************************************************************/
int any_shared_split_points(SEAMS seam_list, SEAM *seam) {
int length;
int index;
length = array_count (seam_list);
for (index = 0; index < length; index++)
if (shared_split_points ((SEAM *) array_value (seam_list, index), seam))
return TRUE;
return FALSE;
}
/**********************************************************************
* check_blob
*
* Return true if blob has a non whole outline.
**********************************************************************/
int check_blob(TBLOB *blob) {
TESSLINE *outline;
EDGEPT *edgept;
for (outline = blob->outlines; outline != NULL; outline = outline->next) {
edgept = outline->loop;
do {
if (edgept == NULL)
break;
edgept = edgept->next;
}
while (edgept != outline->loop);
if (edgept == NULL)
return 1;
}
return 0;
}
/**********************************************************************
* improve_one_blob
*
* Start with the current word of blobs and its classification. Find
* the worst blobs and try to divide it up to improve the ratings.
*********************************************************************/
namespace tesseract {
bool Wordrec::improve_one_blob(TWERD *word,
BLOB_CHOICE_LIST_VECTOR *char_choices,
int fx,
inT32 *blob_number,
SEAMS *seam_list,
DANGERR *fixpt,
bool split_next_to_fragment) {
TBLOB *pblob;
TBLOB *blob;
inT16 x = 0;
float rating_ceiling = MAX_FLOAT32;
BLOB_CHOICE_LIST *answer;
BLOB_CHOICE_IT answer_it;
SEAM *seam;
do {
*blob_number = select_blob_to_split(*char_choices, rating_ceiling,
split_next_to_fragment);
if (chop_debug)
cprintf("blob_number = %d\n", *blob_number);
if (*blob_number == -1)
return false;
seam = attempt_blob_chop (word, *blob_number, *seam_list);
if (seam != NULL)
break;
/* Must split null blobs */
answer = char_choices->get(*blob_number);
if (answer == NULL)
return false;
answer_it.set_to_list(answer);
rating_ceiling = answer_it.data()->rating(); // try a different blob
} while (!tord_blob_skip);
/* Split OK */
for (blob = word->blobs, pblob = NULL; x < *blob_number; x++) {
pblob = blob;
blob = blob->next;
}
*seam_list =
insert_seam (*seam_list, *blob_number, seam, blob, word->blobs);
delete char_choices->get(*blob_number);
answer = classify_blob(pblob, blob, blob->next, NULL, "improve 1:", Red);
char_choices->insert(answer, *blob_number);
answer = classify_blob(blob, blob->next, blob->next->next, NULL,
"improve 2:", Yellow);
char_choices->set(answer, *blob_number + 1);
return true;
}
/**********************************************************************
* modify_blob_choice
*
* Takes a blob and its chop index, converts that chop index to a
* unichar_id, and stores the chop index in place of the blob's
* original unichar_id.
*********************************************************************/
void Wordrec::modify_blob_choice(BLOB_CHOICE_LIST *answer,
int chop_index) {
char chop_index_string[2];
if (chop_index <= 9) {
snprintf(chop_index_string, sizeof(chop_index_string), "%d", chop_index);
} else {
chop_index_string[0] = static_cast<char>('A' - 10 + chop_index);
chop_index_string[1] = '\0';
}
UNICHAR_ID unichar_id = unicharset.unichar_to_id(chop_index_string);
ASSERT_HOST(unichar_id!=INVALID_UNICHAR_ID);
BLOB_CHOICE_IT answer_it(answer);
BLOB_CHOICE *modified_blob = new BLOB_CHOICE(unichar_id,
answer_it.data()->rating(),
answer_it.data()->certainty(),
answer_it.data()->config(),
answer_it.data()->script_id());
answer->clear();
answer_it.set_to_list(answer);
answer_it.add_after_then_move(modified_blob);
}
/**********************************************************************
* chop_one_blob
*
* Start with the current one-blob word and its classification. Find
* the worst blobs and try to divide it up to improve the ratings.
* Used for testing chopper.
*********************************************************************/
bool Wordrec::chop_one_blob(TWERD *word,
BLOB_CHOICE_LIST_VECTOR *char_choices,
inT32 *blob_number,
SEAMS *seam_list,
int *right_chop_index) {
TBLOB *pblob;
TBLOB *blob;
inT16 x = 0;
float rating_ceiling = MAX_FLOAT32;
BLOB_CHOICE_LIST *answer;
BLOB_CHOICE_IT answer_it;
SEAM *seam;
UNICHAR_ID unichar_id = 0;
int left_chop_index = 0;
do {
*blob_number = select_blob_to_split(*char_choices, rating_ceiling,
false);
if (chop_debug)
cprintf("blob_number = %d\n", *blob_number);
if (*blob_number == -1)
return false;
seam = attempt_blob_chop(word, *blob_number, *seam_list);
if (seam != NULL)
break;
/* Must split null blobs */
answer = char_choices->get(*blob_number);
if (answer == NULL)
return false;
answer_it.set_to_list(answer);
rating_ceiling = answer_it.data()->rating(); // try a different blob
} while (!tord_blob_skip);
/* Split OK */
for (blob = word->blobs, pblob = NULL; x < *blob_number; x++) {
pblob = blob;
blob = blob->next;
}
*seam_list =
insert_seam(*seam_list, *blob_number, seam, blob, word->blobs);
answer = char_choices->get(*blob_number);
answer_it.set_to_list(answer);
unichar_id = answer_it.data()->unichar_id();
left_chop_index = atoi(unicharset.id_to_unichar(unichar_id));
delete char_choices->get(*blob_number);
// combine confidence w/ serial #
answer = classify_blob(pblob, blob, blob->next, NULL, "improve 1:", Red);
modify_blob_choice(answer, left_chop_index);
char_choices->insert(answer, *blob_number);
answer = classify_blob(blob, blob->next, blob->next->next, NULL,
"improve 2:", Yellow);
modify_blob_choice(answer, ++*right_chop_index);
char_choices->set(answer, *blob_number + 1);
return true;
}
} // namespace tesseract
/**********************************************************************
* check_seam_order
*
* Make sure that each of the splits in this seam match to outlines
* in this blob. If any of the splits could not correspond to this
* blob then there is a problem (and FALSE should be returned to the
* caller).
**********************************************************************/
inT16 check_seam_order(TBLOB *blob, SEAM *seam) {
TESSLINE *outline;
TESSLINE *last_outline;
inT8 found_em[3];
if (seam->split1 == NULL || seam->split1 == NULL || blob == NULL)
return (TRUE);
found_em[0] = found_em[1] = found_em[2] = FALSE;
for (outline = blob->outlines; outline; outline = outline->next) {
if (!found_em[0] &&
((seam->split1 == NULL) ||
is_split_outline (outline, seam->split1))) {
found_em[0] = TRUE;
}
if (!found_em[1] &&
((seam->split2 == NULL) ||
is_split_outline (outline, seam->split2))) {
found_em[1] = TRUE;
}
if (!found_em[2] &&
((seam->split3 == NULL) ||
is_split_outline (outline, seam->split3))) {
found_em[2] = TRUE;
}
last_outline = outline;
}
if (!found_em[0] || !found_em[1] || !found_em[2])
return (FALSE);
else
return (TRUE);
}
/**********************************************************************
* chop_word_main
*
* Classify the blobs in this word and permute the results. Find the
* worst blob in the word and chop it up. Continue this process until
* a good answer has been found or all the blobs have been chopped up
* enough. Return the word level ratings.
**********************************************************************/
namespace tesseract {
BLOB_CHOICE_LIST_VECTOR *Wordrec::chop_word_main(register TWERD *word,
int fx,
WERD_CHOICE *best_choice,
WERD_CHOICE *raw_choice,
BOOL8 tester,
BOOL8 trainer) {
TBLOB *pblob;
TBLOB *blob;
int index;
int did_chopping;
float rating_limit = 1000.0;
STATE state;
SEAMS seam_list = start_seam_list(word->blobs);
BLOB_CHOICE_LIST *match_result;
MATRIX *ratings = NULL;
DANGERR fixpt; /*dangerous ambig */
inT32 state_count; //no of states
inT32 bit_count; //no of bits
static STATE best_state;
static STATE chop_states[64]; //in between states
state_count = 0;
best_choice->make_bad();
raw_choice->make_bad();
BLOB_CHOICE_LIST_VECTOR *char_choices = new BLOB_CHOICE_LIST_VECTOR();
did_chopping = 0;
for (blob = word->blobs, pblob = NULL, index = 0;
blob != NULL; blob = blob->next, index++) {
match_result = classify_blob(pblob, blob, blob->next, NULL,
"chop_word:", Green);
if (match_result == NULL)
cprintf("Null classifier output!\n");
*char_choices += match_result;
pblob = blob;
}
bit_count = index - 1;
getDict().permute_characters(*char_choices, rating_limit,
best_choice, raw_choice);
set_n_ones(&state, char_choices->length() - 1);
if (matcher_fp != NULL) {
bits_in_states = bit_count;
chop_states[state_count] = state;
state_count++;
}
bool replaced = false;
if (!getDict().AcceptableChoice(char_choices, best_choice, *raw_choice,
&fixpt, CHOPPER_CALLER, &replaced) ||
((tester || trainer) &&
strcmp(word->correct, best_choice->unichar_string().string()))) {
if (replaced) update_blob_classifications(word, *char_choices);
did_chopping = 1;
if (first_pass)
words_chopped1++;
else
words_chopped2++;
if (chop_enable)
improve_by_chopping(word,
char_choices,
fx,
&state,
best_choice,
raw_choice,
&seam_list,
&fixpt,
chop_states,
&state_count);
if (chop_debug)
print_seams ("Final seam list:", seam_list);
// The force_word_assoc is almost redundant to enable_assoc. However,
// it is not conditioned on the dict behavior. For CJK, we need to force
// the associator to be invoked. When we figure out the exact behavior
// of dict on CJK, we can remove the flag if it turns out to be redundant.
if ((wordrec_enable_assoc &&
!getDict().AcceptableChoice(char_choices, best_choice, *raw_choice,
NULL, CHOPPER_CALLER, &replaced)) ||
force_word_assoc ||
((tester || trainer) &&
strcmp(word->correct, best_choice->unichar_string().string()))) {
ratings = word_associator (word->blobs, seam_list, &state, fx,
best_choice, raw_choice, word->correct,
/*0, */ &fixpt, &best_state);
}
bits_in_states = bit_count + state_count - 1;
}
if (replaced) update_blob_classifications(word, *char_choices);
char_choices =
rebuild_current_state(word->blobs, seam_list, &state, char_choices, fx,
(did_chopping || tester || trainer), *best_choice,
ratings);
if (ratings != NULL) {
ratings->delete_matrix_pointers();
delete ratings;
}
if (seam_list != NULL)
free_seam_list(seam_list);
if (matcher_fp != NULL) {
best_state = state;
}
getDict().FilterWordChoices();
return char_choices;
}
/**********************************************************************
* improve_by_chopping
*
* Start with the current word of blobs and its classification. Find
* the worst blobs and try to divide them up to improve the ratings.
* As long as ratings are produced by the new blob splitting. When
* all the splitting has been accomplished all the ratings memory is
* reclaimed.
**********************************************************************/
void Wordrec::improve_by_chopping(register TWERD *word,
BLOB_CHOICE_LIST_VECTOR *char_choices,
int fx,
STATE *best_state,
WERD_CHOICE *best_choice,
WERD_CHOICE *raw_choice,
SEAMS *seam_list,
DANGERR *fixpt,
STATE *chop_states,
inT32 *state_count) {
inT32 blob_number;
inT32 index; //to states
float old_best;
int fixpt_valid = 1;
static inT32 old_count; //from pass1
bool replaced = false;
do { // improvement loop
if (replaced) update_blob_classifications(word, *char_choices);
if (!fixpt_valid)
fixpt->index = -1;
old_best = best_choice->rating();
if (improve_one_blob(word, char_choices, fx, &blob_number, seam_list,
fixpt, (fragments_guide_chopper &&
best_choice->fragment_mark()))) {
getDict().LogNewSplit(blob_number);
getDict().permute_characters(*char_choices, best_choice->rating(),
best_choice, raw_choice);
if (old_best > best_choice->rating()) {
set_n_ones(best_state, char_choices->length() - 1);
fixpt_valid = 1;
}
else {
insert_new_chunk(best_state, blob_number, char_choices->length() - 2);
fixpt_valid = 0;
}
if (*state_count > 0) {
for (index = 0; index < *state_count; index++) {
insert_new_chunk(&chop_states[index], blob_number,
char_choices->length() - 2);
}
set_n_ones(&chop_states[index], char_choices->length() - 1);
(*state_count)++;
}
if (chop_debug)
print_state ("best state = ",
best_state, count_blobs (word->blobs) - 1);
if (first_pass)
chops_performed1++;
else
chops_performed2++;
} else {
break;
}
} while (!getDict().AcceptableChoice(char_choices, best_choice, *raw_choice,
fixpt, CHOPPER_CALLER, &replaced) &&
!tord_blob_skip && char_choices->length() < MAX_NUM_CHUNKS);
if (replaced) update_blob_classifications(word, *char_choices);
old_count = *state_count;
if (!fixpt_valid)
fixpt->index = -1;
}
/**********************************************************************
* select_blob_to_split
*
* These are the results of the last classification. Find a likely
* place to apply splits.
**********************************************************************/
inT16 Wordrec::select_blob_to_split(const BLOB_CHOICE_LIST_VECTOR &char_choices,
float rating_ceiling,
bool split_next_to_fragment) {
BLOB_CHOICE_IT blob_choice_it;
BLOB_CHOICE *blob_choice;
BLOB_CHOICE_IT temp_it;
int x;
float worst = -MAX_FLOAT32;
int worst_index = -1;
float worst_near_fragment = -MAX_FLOAT32;
int worst_index_near_fragment = -1;
const CHAR_FRAGMENT **fragments = NULL;
if (chop_debug) {
if (rating_ceiling < MAX_FLOAT32)
cprintf("rating_ceiling = %8.4f\n", rating_ceiling);
else
cprintf("rating_ceiling = No Limit\n");
}
if (split_next_to_fragment && char_choices.length() > 0) {
fragments = new const CHAR_FRAGMENT *[char_choices.length()];
if (char_choices.get(0) != NULL) {
temp_it.set_to_list(char_choices.get(0));
fragments[0] = getDict().getUnicharset().get_fragment(
temp_it.data()->unichar_id());
} else {
fragments[0] = NULL;
}
}
for (x = 0; x < char_choices.length(); ++x) {
if (char_choices.get(x) == NULL) {
if (fragments != NULL) {
delete[] fragments;
}
return x;
} else {
blob_choice_it.set_to_list(char_choices.get(x));
blob_choice = blob_choice_it.data();
// Populate fragments for the following position.
if (split_next_to_fragment && x+1 < char_choices.length()) {
if (char_choices.get(x+1) != NULL) {
temp_it.set_to_list(char_choices.get(x+1));
fragments[x+1] = getDict().getUnicharset().get_fragment(
temp_it.data()->unichar_id());
} else {
fragments[x+1] = NULL;
}
}
if (blob_choice->rating() < rating_ceiling &&
blob_choice->certainty() < tessedit_certainty_threshold) {
// Update worst and worst_index.
if (blob_choice->rating() > worst) {
worst_index = x;
worst = blob_choice->rating();
}
if (split_next_to_fragment) {
// Update worst_near_fragment and worst_index_near_fragment.
bool expand_following_fragment =
(x + 1 < char_choices.length() &&
fragments[x+1] != NULL && !fragments[x+1]->is_beginning());
bool expand_preceding_fragment =
(x > 0 && fragments[x-1] != NULL && !fragments[x-1]->is_ending());
if ((expand_following_fragment || expand_preceding_fragment) &&
blob_choice->rating() > worst_near_fragment) {
worst_index_near_fragment = x;
worst_near_fragment = blob_choice->rating();
if (chop_debug) {
cprintf("worst_index_near_fragment=%d"
" expand_following_fragment=%d"
" expand_preceding_fragment=%d\n",
worst_index_near_fragment,
expand_following_fragment,
expand_preceding_fragment);
}
}
}
}
}
}
if (fragments != NULL) {
delete[] fragments;
}
// TODO(daria): maybe a threshold of badness for
// worst_near_fragment would be useful.
return worst_index_near_fragment != -1 ?
worst_index_near_fragment : worst_index;
}
} // namespace tesseract
/**********************************************************************
* start_seam_list
*
* Initialize a list of seams that match the original number of blobs
* present in the starting segmentation. Each of the seams created
* by this routine have location information only.
**********************************************************************/
SEAMS start_seam_list(TBLOB *blobs) {
TBLOB *blob;
SEAMS seam_list;
TPOINT topleft;
TPOINT botright;
int location;
/* Seam slot per char */
seam_list = new_seam_list ();
for (blob = blobs; blob->next != NULL; blob = blob->next) {
blob_bounding_box(blob, &topleft, &botright);
location = botright.x;
blob_bounding_box (blob->next, &topleft, &botright);
location += topleft.x;
location /= 2;
seam_list = add_seam (seam_list,
new_seam (0.0, location, NULL, NULL, NULL));
}
return (seam_list);
}
/**********************************************************************
* total_containment
*
* Check to see if one of these outlines is totally contained within
* the bounding box of the other.
**********************************************************************/
inT16 total_containment(TBLOB *blob1, TBLOB *blob2) {
TPOINT topleft1;
TPOINT botright1;
TPOINT topleft2;
TPOINT botright2;
blob_bounding_box(blob1, &topleft1, &botright1);
blob_bounding_box(blob2, &topleft2, &botright2);
return (bounds_inside (topleft1, botright1, topleft2, botright2) ||
bounds_inside (topleft2, botright2, topleft1, botright1));
}
/**********************************************************************
* word_associator
*
* Reassociate and classify the blobs in a word. Continue this process
* until a good answer is found or all the possibilities have been tried.
**********************************************************************/
namespace tesseract {
MATRIX *Wordrec::word_associator(TBLOB *blobs,
SEAMS seams,
STATE *state,
int fxid,
WERD_CHOICE *best_choice,
WERD_CHOICE *raw_choice,
char *correct,
DANGERR *fixpt,
STATE *best_state) {
CHUNKS_RECORD chunks_record;
BLOB_WEIGHTS blob_weights;
int x;
int num_chunks;
BLOB_CHOICE_IT blob_choice_it;
num_chunks = array_count (seams) + 1;
chunks_record.chunks = blobs;
chunks_record.splits = seams;
chunks_record.ratings = record_piece_ratings (blobs);
chunks_record.char_widths = blobs_widths (blobs);
chunks_record.chunk_widths = blobs_widths (blobs);
chunks_record.fx = fxid;
/* Save chunk weights */
for (x = 0; x < num_chunks; x++) {
BLOB_CHOICE_LIST* choices = get_piece_rating(chunks_record.ratings,
blobs, seams, x, x);
blob_choice_it.set_to_list(choices);
//This is done by Jetsoft. Divide by zero is possible.
if (blob_choice_it.data()->certainty() == 0) {
blob_weights[x]=0;
} else {
blob_weights[x] =
-(inT16) (10 * blob_choice_it.data()->rating() /
blob_choice_it.data()->certainty());
}
}
chunks_record.weights = blob_weights;
if (chop_debug)
chunks_record.ratings->print(getDict().getUnicharset());
best_first_search(&chunks_record,
best_choice,
raw_choice,
state,
fixpt,
best_state);
free_widths (chunks_record.chunk_widths);
free_widths (chunks_record.char_widths);
return chunks_record.ratings;
}
} // namespace tesseract
Reference in New Issue View Git Blame Copy Permalink