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(¤t_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);
}
inT16 fp_eval_word_spacing(WERD_RES_LIST &word_res_list) {
WERD_RES_IT word_it(&word_res_list);
WERD_RES *word;
PBLOB_IT blob_it;
inT16 word_length;
inT16 score = 0;
inT16 i;
inT16 offset;
const char *chs;
float small_limit = bln_x_height * fixsp_small_outlines_size;
if (!fixsp_fp_eval)
return (eval_word_spacing (word_res_list));
for (word_it.mark_cycle_pt (); !word_it.cycled_list (); word_it.forward ()) {
word = word_it.data ();
word_length = word->reject_map.length ();
chs = word->best_choice->string ().string ();
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 (chs) > 0)) {
blob_it.set_to_list (word->outword->blob_list ());
for (i = 0, offset = 0; i < word_length;
offset += word->best_choice->lengths()[i++], blob_it.forward ()) {
if ((chs[offset] == ' ') ||
(blob_noise_score (blob_it.data ()) < small_limit))
score -= 1; //penalise possibly erroneous non-space
else if (word->reject_map[i].accepted ())
score++;
}
}
}
if (score < 0)
score = 0;
return score;
}
