/****************************************************************** * 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 #include "reject.h" #include "statistc.h" #include "genblob.h" #include "control.h" #include "fixspace.h" #include "tessvars.h" #include "tessbox.h" #include "secname.h" #include "globals.h" #include "tesseractclass.h" #define EXTERN EXTERN BOOL_VAR (fixsp_check_for_fp_noise_space, TRUE, "Try turning noise to space in fixed pitch"); EXTERN BOOL_VAR (fixsp_fp_eval, TRUE, "Use alternate evaluation for fp"); EXTERN BOOL_VAR (fixsp_noise_score_fixing, TRUE, "More sophisticated?"); EXTERN INT_VAR (fixsp_non_noise_limit, 1, "How many non-noise blbs either side?"); EXTERN double_VAR (fixsp_small_outlines_size, 0.28, "Small if lt xht x this"); EXTERN BOOL_VAR (fixsp_ignore_punct, TRUE, "In uniform spacing calc"); EXTERN BOOL_VAR (fixsp_numeric_fix, TRUE, "Try to deal with numeric punct"); EXTERN BOOL_VAR (fixsp_prefer_joined_1s, TRUE, "Arbitrary boost"); EXTERN BOOL_VAR (tessedit_test_uniform_wd_spacing, FALSE, "Limit context word spacing"); EXTERN BOOL_VAR (tessedit_prefer_joined_punct, FALSE, "Reward punctation joins"); EXTERN INT_VAR (fixsp_done_mode, 1, "What constitues done for spacing"); EXTERN INT_VAR (debug_fix_space_level, 0, "Contextual fixspace debug"); EXTERN STRING_VAR (numeric_punctuation, ".,", "Punct. chs expected WITHIN numbers"); #define PERFECT_WERDS 999 #define MAXSPACING 128 /*max expected spacing in pix */ /************************************************************************* * 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. *************************************************************************/ namespace tesseract { void Tesseract::fix_fuzzy_spaces( //find fuzzy words //progress monitor volatile ETEXT_DESC *monitor, //count of words in doc inT32 word_count, PAGE_RES *page_res) { BLOCK_RES_IT block_res_it; //iterators 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 (!word_res_it_from.at_last ()) { word_res_it_to = word_res_it_from; prevent_null_wd_fixsp = word_res->word->gblob_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; } 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->gblob_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->gblob_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( //space explorer 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); } } // 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; for (word_it.mark_cycle_pt (); !word_it.cycled_list (); word_it.forward ()) { word = word_it.data (); if ((!word->part_of_combo) && (word->outword == NULL)) classify_word_pass2(word, block, row); } } /************************************************************************* * 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) && (!tessedit_test_uniform_wd_spacing || ((word->best_choice->permuter () == NUMBER_PERM) || uniformly_spaced (word)))) { prev_word_done = TRUE; prev_word_score = word_len; } else { prev_word_done = FALSE; prev_word_score = 0; } if (fixsp_prefer_joined_1s) { /* 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 (unicharset.get_isdigit(word->best_choice->unichar_string().string() + offset, word->best_choice->unichar_lengths()[i]) || (fixsp_numeric_fix && (word->best_choice->permuter () == NUMBER_PERM) && STRING (numeric_punctuation).contains (word->best_choice->unichar_string().string()[offset]))); } } // namespace tesseract /************************************************************************* * 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 = -1; 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 >= 0) { gap = box.left () - prev_right; if (gap < min_gap) min_gap = gap; } prev_right = box.right (); } } if (min_gap < MAX_INT16) { prev_right = -1; //back to start word_it.set_to_list (&words); for (; //cant use cycle pt due to inserted combos at start of list (prev_right < 0) || !word_it.at_first (); word_it.forward ()) { word = word_it.data (); if (!word->part_of_combo) { box = word->word->bounding_box (); if (prev_right >= 0) { 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 blbs to combo //old combo no longer needed delete word_it.extract (); } else { //Cpy current wd to combo combo->copy_on (word); word->part_of_combo = TRUE; } combo->done = FALSE; if (combo->outword != NULL) { delete combo->outword; delete combo->best_choice; delete combo->raw_choice; combo->outword = NULL; combo->best_choice = NULL; combo->raw_choice = NULL; } } else //catch up prev_word_it = word_it; } prev_right = box.right (); } } } else words.clear (); //signal termination } void dump_words(WERD_RES_LIST &perm, inT16 score, inT16 mode, BOOL8 improved) { WERD_RES_IT word_res_it(&perm); static STRING initial_str; if (debug_fix_space_level > 0) { if (mode == 1) { initial_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) { initial_str += word_res_it.data()->best_choice->unichar_string(); initial_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\" => \"", initial_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 } } /************************************************************************* * 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 < 64/5 = 13 and all others are <= 0 * **** REMEMBER - WE'RE NOW WORKING WITH A BLN WERD !!! *************************************************************************/ BOOL8 uniformly_spaced( //sensible word WERD_RES *word) { PBLOB_IT blob_it; 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 = "\"`',.:;"; blob_it.set_to_list (word->outword->blob_list ()); for (blob_it.mark_cycle_pt (); !blob_it.cycled_list (); blob_it.forward ()) { box = blob_it.data ()->bounding_box (); if ((prev_right > -MAX_INT16) && (!fixsp_ignore_punct || (!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 * bln_x_height / 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 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; } /************************************************************************* * fix_sp_fp_word() * Test the current word to see if it can be split by deleting noise blobs. If * so, do the buisiness. * Return with the iterator pointing to the same place if the word is unchanged, * or the last of the replacement words. *************************************************************************/ namespace tesseract { 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 (!fixsp_check_for_fp_noise_space || 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; #ifndef SECURE_NAMES if (debug_fix_space_level > 1) { tprintf ("FP fixspace working on \"%s\"\n", word_res->best_choice->unichar_string().string()); } #endif gblob_sort_list ((PBLOB_LIST *) word_res->word->rej_cblob_list (), FALSE); 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(¤t_perm); WERD_RES *old_word_res; WERD_RES *new_word_res; inT16 current_score; BOOL8 improved = FALSE; //default score best_score = fp_eval_word_spacing (best_perm); dump_words (best_perm, best_score, 1, improved); new_word_res = new WERD_RES; old_word_res = best_perm_it.data (); //Kludge to force deep copy old_word_res->combination = TRUE; *new_word_res = *old_word_res; //deep copy //Undo kludge old_word_res->combination = FALSE; //Undo kludge new_word_res->combination = FALSE; 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(¤t_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); } } // namespace tesseract /************************************************************************* * 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 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 blb of noisiest wd int blob_index; //of wds noisiest blb float noise_score; //of wds noisiest blb 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 blb 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 ()); } worst_word_it.add_before_then_move (new WERD_RES (new_word)); word_res->done = FALSE; if (word_res->outword != NULL) { delete word_res->outword; delete word_res->best_choice; delete word_res->raw_choice; word_res->outword = NULL; word_res->best_choice = NULL; word_res->raw_choice = NULL; } } inT16 worst_noise_blob(WERD_RES *word_res, float *worst_noise_score) { PBLOB_IT blob_it; inT16 blob_count; 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 = bln_x_height * fixsp_small_outlines_size; float non_noise_limit = bln_x_height * 0.8; blob_it.set_to_list (word_res->outword->blob_list ()); //normalised blob_count = blob_it.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++, blob_it.forward ()) { if (word_res->reject_map[i].accepted ()) noise_score[i] = non_noise_limit; else noise_score[i] = blob_noise_score (blob_it.data ()); 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 blob_noise_score(PBLOB *blob) { OUTLINE_IT outline_it; TBOX box; //BB of outline inT16 outline_count = 0; inT16 max_dimension; inT16 largest_outline_dimension = 0; outline_it.set_to_list (blob->out_list ()); for (outline_it.mark_cycle_pt (); !outline_it.cycled_list (); outline_it.forward ()) { outline_count++; box = outline_it.data ()->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 (fixsp_noise_score_fixing) { if (outline_count > 5) //penalise LOTS of blobs largest_outline_dimension *= 2; box = blob->bounding_box (); if ((box.bottom () > bln_baseline_offset * 4) || (box.top () < bln_baseline_offset / 2)) //Lax blob is if high or low largest_outline_dimension /= 2; } return largest_outline_dimension; } void fixspace_dbg(WERD_RES *word) { TBOX box = word->word->bounding_box (); BOOL8 show_map_detail = FALSE; inT16 i; box.print (); #ifndef SECURE_NAMES tprintf (" \"%s\" ", word->best_choice->unichar_string().string ()); tprintf ("Blob count: %d (word); %d/%d (outword)\n", word->word->gblob_list ()->length (), word->outword->gblob_list ()->length (), word->outword->rej_blob_list ()->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"); #endif } /************************************************************************* * 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; PBLOB_IT blob_it; inT16 word_length; inT16 score = 0; inT16 i; 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(); 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->best_choice)) > 0)) { blob_it.set_to_list(word->outword->blob_list()); UNICHAR_ID space = getDict().getUnicharset().unichar_to_id(" "); for (i = 0; i < word->best_choice->length(); ++i, blob_it.forward()) { if (word->best_choice->unichar_id(i) == space || (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; } } // namespace tesseract