forked from aspotashev/tesseract-ocr-cmake
-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathlanguage_model.cpp
1458 lines (1388 loc) · 61.9 KB
/
language_model.cpp
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
///////////////////////////////////////////////////////////////////////
// File: language_model.cpp
// Description: Functions that utilize the knowledge about the properties,
// structure and statistics of the language to help recognition.
// Author: Daria Antonova
// Created: Mon Nov 11 11:26:43 PST 2009
//
// (C) Copyright 2009, Google Inc.
// 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 <math.h>
#include "language_model.h"
#include "dawg.h"
#include "freelist.h"
#include "intproto.h"
#include "helpers.h"
#include "lm_state.h"
#include "lm_pain_points.h"
#include "matrix.h"
#include "params.h"
#include "params_training_featdef.h"
namespace tesseract {
const float LanguageModel::kMaxAvgNgramCost = 25.0f;
LanguageModel::LanguageModel(const UnicityTable<FontInfo> *fontinfo_table,
Dict *dict)
: INT_MEMBER(language_model_debug_level, 0, "Language model debug level",
dict->getCCUtil()->params()),
BOOL_INIT_MEMBER(language_model_ngram_on, false,
"Turn on/off the use of character ngram model",
dict->getCCUtil()->params()),
INT_MEMBER(language_model_ngram_order, 8,
"Maximum order of the character ngram model",
dict->getCCUtil()->params()),
INT_MEMBER(language_model_viterbi_list_max_num_prunable, 10,
"Maximum number of prunable (those for which"
" PrunablePath() is true) entries in each viterbi list"
" recorded in BLOB_CHOICEs",
dict->getCCUtil()->params()),
INT_MEMBER(language_model_viterbi_list_max_size, 500,
"Maximum size of viterbi lists recorded in BLOB_CHOICEs",
dict->getCCUtil()->params()),
double_MEMBER(language_model_ngram_small_prob, 0.000001,
"To avoid overly small denominators use this as the "
"floor of the probability returned by the ngram model.",
dict->getCCUtil()->params()),
double_MEMBER(language_model_ngram_nonmatch_score, -40.0,
"Average classifier score of a non-matching unichar.",
dict->getCCUtil()->params()),
BOOL_MEMBER(language_model_ngram_use_only_first_uft8_step, false,
"Use only the first UTF8 step of the given string"
" when computing log probabilities.",
dict->getCCUtil()->params()),
double_MEMBER(language_model_ngram_scale_factor, 0.03,
"Strength of the character ngram model relative to the"
" character classifier ",
dict->getCCUtil()->params()),
double_MEMBER(language_model_ngram_rating_factor, 16.0,
"Factor to bring log-probs into the same range as ratings"
" when multiplied by outline length ",
dict->getCCUtil()->params()),
BOOL_MEMBER(language_model_ngram_space_delimited_language, true,
"Words are delimited by space",
dict->getCCUtil()->params()),
INT_MEMBER(language_model_min_compound_length, 3,
"Minimum length of compound words",
dict->getCCUtil()->params()),
double_MEMBER(language_model_penalty_non_freq_dict_word, 0.1,
"Penalty for words not in the frequent word dictionary",
dict->getCCUtil()->params()),
double_MEMBER(language_model_penalty_non_dict_word, 0.15,
"Penalty for non-dictionary words",
dict->getCCUtil()->params()),
double_MEMBER(language_model_penalty_punc, 0.2,
"Penalty for inconsistent punctuation",
dict->getCCUtil()->params()),
double_MEMBER(language_model_penalty_case, 0.1,
"Penalty for inconsistent case",
dict->getCCUtil()->params()),
double_MEMBER(language_model_penalty_script, 0.5,
"Penalty for inconsistent script",
dict->getCCUtil()->params()),
double_MEMBER(language_model_penalty_chartype, 0.3,
"Penalty for inconsistent character type",
dict->getCCUtil()->params()),
// TODO(daria, rays): enable font consistency checking
// after improving font analysis.
double_MEMBER(language_model_penalty_font, 0.00,
"Penalty for inconsistent font",
dict->getCCUtil()->params()),
double_MEMBER(language_model_penalty_spacing, 0.05,
"Penalty for inconsistent spacing",
dict->getCCUtil()->params()),
double_MEMBER(language_model_penalty_increment, 0.01,
"Penalty increment",
dict->getCCUtil()->params()),
INT_MEMBER(wordrec_display_segmentations, 0, "Display Segmentations",
dict->getCCUtil()->params()),
BOOL_INIT_MEMBER(language_model_use_sigmoidal_certainty, false,
"Use sigmoidal score for certainty",
dict->getCCUtil()->params()),
fontinfo_table_(fontinfo_table), dict_(dict),
fixed_pitch_(false), max_char_wh_ratio_(0.0),
acceptable_choice_found_(false) {
ASSERT_HOST(dict_ != NULL);
dawg_args_ = new DawgArgs(NULL, new DawgPositionVector(), NO_PERM);
very_beginning_active_dawgs_ = new DawgPositionVector();
beginning_active_dawgs_ = new DawgPositionVector();
}
LanguageModel::~LanguageModel() {
delete very_beginning_active_dawgs_;
delete beginning_active_dawgs_;
delete dawg_args_->updated_dawgs;
delete dawg_args_;
}
void LanguageModel::InitForWord(const WERD_CHOICE *prev_word,
bool fixed_pitch, float max_char_wh_ratio,
float rating_cert_scale) {
fixed_pitch_ = fixed_pitch;
max_char_wh_ratio_ = max_char_wh_ratio;
rating_cert_scale_ = rating_cert_scale;
acceptable_choice_found_ = false;
correct_segmentation_explored_ = false;
// Initialize vectors with beginning DawgInfos.
very_beginning_active_dawgs_->clear();
dict_->init_active_dawgs(very_beginning_active_dawgs_, false);
beginning_active_dawgs_->clear();
dict_->default_dawgs(beginning_active_dawgs_, false);
// Fill prev_word_str_ with the last language_model_ngram_order
// unichars from prev_word.
if (language_model_ngram_on) {
if (prev_word != NULL && prev_word->unichar_string() != NULL) {
prev_word_str_ = prev_word->unichar_string();
if (language_model_ngram_space_delimited_language) prev_word_str_ += ' ';
} else {
prev_word_str_ = " ";
}
const char *str_ptr = prev_word_str_.string();
const char *str_end = str_ptr + prev_word_str_.length();
int step;
prev_word_unichar_step_len_ = 0;
while (str_ptr != str_end && (step = UNICHAR::utf8_step(str_ptr))) {
str_ptr += step;
++prev_word_unichar_step_len_;
}
ASSERT_HOST(str_ptr == str_end);
}
}
// Helper scans the collection of predecessors for competing siblings that
// have the same letter with the opposite case, setting competing_vse.
static void ScanParentsForCaseMix(const UNICHARSET& unicharset,
LanguageModelState* parent_node) {
if (parent_node == NULL) return;
ViterbiStateEntry_IT vit(&parent_node->viterbi_state_entries);
for (vit.mark_cycle_pt(); !vit.cycled_list(); vit.forward()) {
ViterbiStateEntry* vse = vit.data();
vse->competing_vse = NULL;
UNICHAR_ID unichar_id = vse->curr_b->unichar_id();
if (unicharset.get_isupper(unichar_id) ||
unicharset.get_islower(unichar_id)) {
UNICHAR_ID other_case = unicharset.get_other_case(unichar_id);
if (other_case == unichar_id) continue; // Not in unicharset.
// Find other case in same list. There could be multiple entries with
// the same unichar_id, but in theory, they should all point to the
// same BLOB_CHOICE, and that is what we will be using to decide
// which to keep.
ViterbiStateEntry_IT vit2(&parent_node->viterbi_state_entries);
for (vit2.mark_cycle_pt(); !vit2.cycled_list() &&
vit2.data()->curr_b->unichar_id() != other_case;
vit2.forward()) {}
if (!vit2.cycled_list()) {
vse->competing_vse = vit2.data();
}
}
}
}
// Helper returns true if the given choice has a better case variant before
// it in the choice_list that is not distinguishable by size.
static bool HasBetterCaseVariant(const UNICHARSET& unicharset,
const BLOB_CHOICE* choice,
BLOB_CHOICE_LIST* choices) {
UNICHAR_ID choice_id = choice->unichar_id();
UNICHAR_ID other_case = unicharset.get_other_case(choice_id);
if (other_case == choice_id || other_case == INVALID_UNICHAR_ID)
return false; // Not upper or lower or not in unicharset.
if (unicharset.SizesDistinct(choice_id, other_case))
return false; // Can be separated by size.
BLOB_CHOICE_IT bc_it(choices);
for (bc_it.mark_cycle_pt(); !bc_it.cycled_list(); bc_it.forward()) {
BLOB_CHOICE* better_choice = bc_it.data();
if (better_choice->unichar_id() == other_case)
return true; // Found an earlier instance of other_case.
else if (better_choice == choice)
return false; // Reached the original choice.
}
return false; // Should never happen, but just in case.
}
// UpdateState has the job of combining the ViterbiStateEntry lists on each
// of the choices on parent_list with each of the blob choices in curr_list,
// making a new ViterbiStateEntry for each sensible path.
// This could be a huge set of combinations, creating a lot of work only to
// be truncated by some beam limit, but only certain kinds of paths will
// continue at the next step:
// paths that are liked by the language model: either a DAWG or the n-gram
// model, where active.
// paths that represent some kind of top choice. The old permuter permuted
// the top raw classifier score, the top upper case word and the top lower-
// case word. UpdateState now concentrates its top-choice paths on top
// lower-case, top upper-case (or caseless alpha), and top digit sequence,
// with allowance for continuation of these paths through blobs where such
// a character does not appear in the choices list.
// GetNextParentVSE enforces some of these models to minimize the number of
// calls to AddViterbiStateEntry, even prior to looking at the language model.
// Thus an n-blob sequence of [l1I] will produce 3n calls to
// AddViterbiStateEntry instead of 3^n.
// Of course it isn't quite that simple as Title Case is handled by allowing
// lower case to continue an upper case initial, but it has to be detected
// in the combiner so it knows which upper case letters are initial alphas.
bool LanguageModel::UpdateState(
bool just_classified,
int curr_col, int curr_row,
BLOB_CHOICE_LIST *curr_list,
LanguageModelState *parent_node,
LMPainPoints *pain_points,
WERD_RES *word_res,
BestChoiceBundle *best_choice_bundle,
BlamerBundle *blamer_bundle) {
if (language_model_debug_level > 0) {
tprintf("\nUpdateState: col=%d row=%d %s",
curr_col, curr_row, just_classified ? "just_classified" : "");
if (language_model_debug_level > 5)
tprintf("(parent=%p)\n", parent_node);
else
tprintf("\n");
}
// Initialize helper variables.
bool word_end = (curr_row+1 >= word_res->ratings->dimension());
bool new_changed = false;
float denom = (language_model_ngram_on) ? ComputeDenom(curr_list) : 1.0f;
const UNICHARSET& unicharset = dict_->getUnicharset();
BLOB_CHOICE *first_lower = NULL;
BLOB_CHOICE *first_upper = NULL;
BLOB_CHOICE *first_digit = NULL;
bool has_alnum_mix = false;
if (parent_node != NULL) {
int result = SetTopParentLowerUpperDigit(parent_node);
if (result < 0) {
if (language_model_debug_level > 0)
tprintf("No parents found to process\n");
return false;
}
if (result > 0)
has_alnum_mix = true;
}
if (!GetTopLowerUpperDigit(curr_list, &first_lower, &first_upper,
&first_digit))
has_alnum_mix = false;;
ScanParentsForCaseMix(unicharset, parent_node);
if (language_model_debug_level > 3 && parent_node != NULL) {
parent_node->Print("Parent viterbi list");
}
LanguageModelState *curr_state = best_choice_bundle->beam[curr_row];
// Call AddViterbiStateEntry() for each parent+child ViterbiStateEntry.
ViterbiStateEntry_IT vit;
BLOB_CHOICE_IT c_it(curr_list);
for (c_it.mark_cycle_pt(); !c_it.cycled_list(); c_it.forward()) {
BLOB_CHOICE* choice = c_it.data();
// TODO(antonova): make sure commenting this out if ok for ngram
// model scoring (I think this was introduced to fix ngram model quirks).
// Skip NULL unichars unless it is the only choice.
//if (!curr_list->singleton() && c_it.data()->unichar_id() == 0) continue;
UNICHAR_ID unichar_id = choice->unichar_id();
if (unicharset.get_fragment(unichar_id)) {
continue; // Skip fragments.
}
// Set top choice flags.
LanguageModelFlagsType blob_choice_flags = kXhtConsistentFlag;
if (c_it.at_first() || !new_changed)
blob_choice_flags |= kSmallestRatingFlag;
if (first_lower == choice) blob_choice_flags |= kLowerCaseFlag;
if (first_upper == choice) blob_choice_flags |= kUpperCaseFlag;
if (first_digit == choice) blob_choice_flags |= kDigitFlag;
if (parent_node == NULL) {
// Process the beginning of a word.
// If there is a better case variant that is not distinguished by size,
// skip this blob choice, as we have no choice but to accept the result
// of the character classifier to distinguish between them, even if
// followed by an upper case.
// With words like iPoc, and other CamelBackWords, the lower-upper
// transition can only be achieved if the classifier has the correct case
// as the top choice, and leaving an initial I lower down the list
// increases the chances of choosing IPoc simply because it doesn't
// include such a transition. iPoc will beat iPOC and ipoc because
// the other words are baseline/x-height inconsistent.
if (HasBetterCaseVariant(unicharset, choice, curr_list))
continue;
// Upper counts as lower at the beginning of a word.
if (blob_choice_flags & kUpperCaseFlag)
blob_choice_flags |= kLowerCaseFlag;
new_changed |= AddViterbiStateEntry(
blob_choice_flags, denom, word_end, curr_col, curr_row,
choice, curr_state, NULL, pain_points,
word_res, best_choice_bundle, blamer_bundle);
} else {
// Get viterbi entries from each parent ViterbiStateEntry.
vit.set_to_list(&parent_node->viterbi_state_entries);
int vit_counter = 0;
vit.mark_cycle_pt();
ViterbiStateEntry* parent_vse = NULL;
LanguageModelFlagsType top_choice_flags;
while ((parent_vse = GetNextParentVSE(just_classified, has_alnum_mix,
c_it.data(), blob_choice_flags,
unicharset, word_res, &vit,
&top_choice_flags)) != NULL) {
// Skip pruned entries and do not look at prunable entries if already
// examined language_model_viterbi_list_max_num_prunable of those.
if (PrunablePath(*parent_vse) &&
(++vit_counter > language_model_viterbi_list_max_num_prunable ||
(language_model_ngram_on && parent_vse->ngram_info->pruned))) {
continue;
}
// If the parent has no alnum choice, (ie choice is the first in a
// string of alnum), and there is a better case variant that is not
// distinguished by size, skip this blob choice/parent, as with the
// initial blob treatment above.
if (!parent_vse->HasAlnumChoice(unicharset) &&
HasBetterCaseVariant(unicharset, choice, curr_list))
continue;
// Create a new ViterbiStateEntry if BLOB_CHOICE in c_it.data()
// looks good according to the Dawgs or character ngram model.
new_changed |= AddViterbiStateEntry(
top_choice_flags, denom, word_end, curr_col, curr_row,
c_it.data(), curr_state, parent_vse, pain_points,
word_res, best_choice_bundle, blamer_bundle);
}
}
}
return new_changed;
}
// Finds the first lower and upper case letter and first digit in curr_list.
// For non-upper/lower languages, alpha counts as upper.
// Uses the first character in the list in place of empty results.
// Returns true if both alpha and digits are found.
bool LanguageModel::GetTopLowerUpperDigit(BLOB_CHOICE_LIST *curr_list,
BLOB_CHOICE **first_lower,
BLOB_CHOICE **first_upper,
BLOB_CHOICE **first_digit) const {
BLOB_CHOICE_IT c_it(curr_list);
const UNICHARSET &unicharset = dict_->getUnicharset();
BLOB_CHOICE *first_unichar = NULL;
for (c_it.mark_cycle_pt(); !c_it.cycled_list(); c_it.forward()) {
UNICHAR_ID unichar_id = c_it.data()->unichar_id();
if (unicharset.get_fragment(unichar_id)) continue; // skip fragments
if (first_unichar == NULL) first_unichar = c_it.data();
if (*first_lower == NULL && unicharset.get_islower(unichar_id)) {
*first_lower = c_it.data();
}
if (*first_upper == NULL && unicharset.get_isalpha(unichar_id) &&
!unicharset.get_islower(unichar_id)) {
*first_upper = c_it.data();
}
if (*first_digit == NULL && unicharset.get_isdigit(unichar_id)) {
*first_digit = c_it.data();
}
}
ASSERT_HOST(first_unichar != NULL);
bool mixed = (*first_lower != NULL || *first_upper != NULL) &&
*first_digit != NULL;
if (*first_lower == NULL) *first_lower = first_unichar;
if (*first_upper == NULL) *first_upper = first_unichar;
if (*first_digit == NULL) *first_digit = first_unichar;
return mixed;
}
// Forces there to be at least one entry in the overall set of the
// viterbi_state_entries of each element of parent_node that has the
// top_choice_flag set for lower, upper and digit using the same rules as
// GetTopLowerUpperDigit, setting the flag on the first found suitable
// candidate, whether or not the flag is set on some other parent.
// Returns 1 if both alpha and digits are found among the parents, -1 if no
// parents are found at all (a legitimate case), and 0 otherwise.
int LanguageModel::SetTopParentLowerUpperDigit(
LanguageModelState *parent_node) const {
if (parent_node == NULL) return -1;
UNICHAR_ID top_id = INVALID_UNICHAR_ID;
ViterbiStateEntry* top_lower = NULL;
ViterbiStateEntry* top_upper = NULL;
ViterbiStateEntry* top_digit = NULL;
ViterbiStateEntry* top_choice = NULL;
float lower_rating = 0.0f;
float upper_rating = 0.0f;
float digit_rating = 0.0f;
float top_rating = 0.0f;
const UNICHARSET &unicharset = dict_->getUnicharset();
ViterbiStateEntry_IT vit(&parent_node->viterbi_state_entries);
for (vit.mark_cycle_pt(); !vit.cycled_list(); vit.forward()) {
ViterbiStateEntry* vse = vit.data();
// INVALID_UNICHAR_ID should be treated like a zero-width joiner, so scan
// back to the real character if needed.
ViterbiStateEntry* unichar_vse = vse;
UNICHAR_ID unichar_id = unichar_vse->curr_b->unichar_id();
float rating = unichar_vse->curr_b->rating();
while (unichar_id == INVALID_UNICHAR_ID &&
unichar_vse->parent_vse != NULL) {
unichar_vse = unichar_vse->parent_vse;
unichar_id = unichar_vse->curr_b->unichar_id();
rating = unichar_vse->curr_b->rating();
}
if (unichar_id != INVALID_UNICHAR_ID) {
if (unicharset.get_islower(unichar_id)) {
if (top_lower == NULL || lower_rating > rating) {
top_lower = vse;
lower_rating = rating;
}
} else if (unicharset.get_isalpha(unichar_id)) {
if (top_upper == NULL || upper_rating > rating) {
top_upper = vse;
upper_rating = rating;
}
} else if (unicharset.get_isdigit(unichar_id)) {
if (top_digit == NULL || digit_rating > rating) {
top_digit = vse;
digit_rating = rating;
}
}
}
if (top_choice == NULL || top_rating > rating) {
top_choice = vse;
top_rating = rating;
top_id = unichar_id;
}
}
if (top_choice == NULL) return -1;
bool mixed = (top_lower != NULL || top_upper != NULL) &&
top_digit != NULL;
if (top_lower == NULL) top_lower = top_choice;
top_lower->top_choice_flags |= kLowerCaseFlag;
if (top_upper == NULL) top_upper = top_choice;
top_upper->top_choice_flags |= kUpperCaseFlag;
if (top_digit == NULL) top_digit = top_choice;
top_digit->top_choice_flags |= kDigitFlag;
top_choice->top_choice_flags |= kSmallestRatingFlag;
if (top_id != INVALID_UNICHAR_ID && dict_->compound_marker(top_id) &&
(top_choice->top_choice_flags &
(kLowerCaseFlag | kUpperCaseFlag | kDigitFlag))) {
// If the compound marker top choice carries any of the top alnum flags,
// then give it all of them, allowing words like I-295 to be chosen.
top_choice->top_choice_flags |=
kLowerCaseFlag | kUpperCaseFlag | kDigitFlag;
}
return mixed ? 1 : 0;
}
// Finds the next ViterbiStateEntry with which the given unichar_id can
// combine sensibly, taking into account any mixed alnum/mixed case
// situation, and whether this combination has been inspected before.
ViterbiStateEntry* LanguageModel::GetNextParentVSE(
bool just_classified, bool mixed_alnum, const BLOB_CHOICE* bc,
LanguageModelFlagsType blob_choice_flags, const UNICHARSET& unicharset,
WERD_RES* word_res, ViterbiStateEntry_IT* vse_it,
LanguageModelFlagsType* top_choice_flags) const {
for (; !vse_it->cycled_list(); vse_it->forward()) {
ViterbiStateEntry* parent_vse = vse_it->data();
// Only consider the parent if it has been updated or
// if the current ratings cell has just been classified.
if (!just_classified && !parent_vse->updated) continue;
if (language_model_debug_level > 2)
parent_vse->Print("Considering");
// If the parent is non-alnum, then upper counts as lower.
*top_choice_flags = blob_choice_flags;
if ((blob_choice_flags & kUpperCaseFlag) &&
!parent_vse->HasAlnumChoice(unicharset)) {
*top_choice_flags |= kLowerCaseFlag;
}
*top_choice_flags &= parent_vse->top_choice_flags;
UNICHAR_ID unichar_id = bc->unichar_id();
const BLOB_CHOICE* parent_b = parent_vse->curr_b;
UNICHAR_ID parent_id = parent_b->unichar_id();
// Digits do not bind to alphas if there is a mix in both parent and current
// or if the alpha is not the top choice.
if (unicharset.get_isdigit(unichar_id) &&
unicharset.get_isalpha(parent_id) &&
(mixed_alnum || *top_choice_flags == 0))
continue; // Digits don't bind to alphas.
// Likewise alphas do not bind to digits if there is a mix in both or if
// the digit is not the top choice.
if (unicharset.get_isalpha(unichar_id) &&
unicharset.get_isdigit(parent_id) &&
(mixed_alnum || *top_choice_flags == 0))
continue; // Alphas don't bind to digits.
// If there is a case mix of the same alpha in the parent list, then
// competing_vse is non-null and will be used to determine whether
// or not to bind the current blob choice.
if (parent_vse->competing_vse != NULL) {
const BLOB_CHOICE* competing_b = parent_vse->competing_vse->curr_b;
UNICHAR_ID other_id = competing_b->unichar_id();
if (language_model_debug_level >= 5) {
tprintf("Parent %s has competition %s\n",
unicharset.id_to_unichar(parent_id),
unicharset.id_to_unichar(other_id));
}
if (unicharset.SizesDistinct(parent_id, other_id)) {
// If other_id matches bc wrt position and size, and parent_id, doesn't,
// don't bind to the current parent.
if (bc->PosAndSizeAgree(*competing_b, word_res->x_height,
language_model_debug_level >= 5) &&
!bc->PosAndSizeAgree(*parent_b, word_res->x_height,
language_model_debug_level >= 5))
continue; // Competing blobchoice has a better vertical match.
}
}
vse_it->forward();
return parent_vse; // This one is good!
}
return NULL; // Ran out of possibilities.
}
bool LanguageModel::AddViterbiStateEntry(
LanguageModelFlagsType top_choice_flags,
float denom,
bool word_end,
int curr_col, int curr_row,
BLOB_CHOICE *b,
LanguageModelState *curr_state,
ViterbiStateEntry *parent_vse,
LMPainPoints *pain_points,
WERD_RES *word_res,
BestChoiceBundle *best_choice_bundle,
BlamerBundle *blamer_bundle) {
ViterbiStateEntry_IT vit;
if (language_model_debug_level > 1) {
tprintf("AddViterbiStateEntry for unichar %s rating=%.4f"
" certainty=%.4f top_choice_flags=0x%x",
dict_->getUnicharset().id_to_unichar(b->unichar_id()),
b->rating(), b->certainty(), top_choice_flags);
if (language_model_debug_level > 5)
tprintf(" parent_vse=%p\n", parent_vse);
else
tprintf("\n");
}
// Check whether the list is full.
if (curr_state != NULL &&
curr_state->viterbi_state_entries_length >=
language_model_viterbi_list_max_size) {
if (language_model_debug_level > 1) {
tprintf("AddViterbiStateEntry: viterbi list is full!\n");
}
return false;
}
// Invoke Dawg language model component.
LanguageModelDawgInfo *dawg_info =
GenerateDawgInfo(word_end, curr_col, curr_row, *b, parent_vse);
float outline_length =
AssociateUtils::ComputeOutlineLength(rating_cert_scale_, *b);
// Invoke Ngram language model component.
LanguageModelNgramInfo *ngram_info = NULL;
if (language_model_ngram_on) {
ngram_info = GenerateNgramInfo(
dict_->getUnicharset().id_to_unichar(b->unichar_id()), b->certainty(),
denom, curr_col, curr_row, outline_length, parent_vse);
ASSERT_HOST(ngram_info != NULL);
}
bool liked_by_language_model = dawg_info != NULL ||
(ngram_info != NULL && !ngram_info->pruned);
// Quick escape if not liked by the language model, can't be consistent
// xheight, and not top choice.
if (!liked_by_language_model && top_choice_flags == 0) {
if (language_model_debug_level > 1) {
tprintf("Language model components very early pruned this entry\n");
}
delete ngram_info;
delete dawg_info;
return false;
}
// Check consistency of the path and set the relevant consistency_info.
LMConsistencyInfo consistency_info(
parent_vse != NULL ? &parent_vse->consistency_info : NULL);
// Start with just the x-height consistency, as it provides significant
// pruning opportunity.
consistency_info.ComputeXheightConsistency(
b, dict_->getUnicharset().get_ispunctuation(b->unichar_id()));
// Turn off xheight consistent flag if not consistent.
if (consistency_info.InconsistentXHeight()) {
top_choice_flags &= ~kXhtConsistentFlag;
}
// Quick escape if not liked by the language model, not consistent xheight,
// and not top choice.
if (!liked_by_language_model && top_choice_flags == 0) {
if (language_model_debug_level > 1) {
tprintf("Language model components early pruned this entry\n");
}
delete ngram_info;
delete dawg_info;
return false;
}
// Compute the rest of the consistency info.
FillConsistencyInfo(curr_col, word_end, b, parent_vse,
word_res, &consistency_info);
if (dawg_info != NULL && consistency_info.invalid_punc) {
consistency_info.invalid_punc = false; // do not penalize dict words
}
// Compute cost of associating the blobs that represent the current unichar.
AssociateStats associate_stats;
ComputeAssociateStats(curr_col, curr_row, max_char_wh_ratio_,
parent_vse, word_res, &associate_stats);
if (parent_vse != NULL) {
associate_stats.shape_cost += parent_vse->associate_stats.shape_cost;
associate_stats.bad_shape |= parent_vse->associate_stats.bad_shape;
}
// Create the new ViterbiStateEntry compute the adjusted cost of the path.
ViterbiStateEntry *new_vse = new ViterbiStateEntry(
parent_vse, b, 0.0, outline_length,
consistency_info, associate_stats, top_choice_flags, dawg_info,
ngram_info, (language_model_debug_level > 0) ?
dict_->getUnicharset().id_to_unichar(b->unichar_id()) : NULL);
new_vse->cost = ComputeAdjustedPathCost(new_vse);
if (language_model_debug_level >= 3)
tprintf("Adjusted cost = %g\n", new_vse->cost);
// Invoke Top Choice language model component to make the final adjustments
// to new_vse->top_choice_flags.
if (!curr_state->viterbi_state_entries.empty() && new_vse->top_choice_flags) {
GenerateTopChoiceInfo(new_vse, parent_vse, curr_state);
}
// If language model components did not like this unichar - return.
bool keep = new_vse->top_choice_flags || liked_by_language_model;
if (!(top_choice_flags & kSmallestRatingFlag) && // no non-top choice paths
consistency_info.inconsistent_script) { // with inconsistent script
keep = false;
}
if (!keep) {
if (language_model_debug_level > 1) {
tprintf("Language model components did not like this entry\n");
}
delete new_vse;
return false;
}
// Discard this entry if it represents a prunable path and
// language_model_viterbi_list_max_num_prunable such entries with a lower
// cost have already been recorded.
if (PrunablePath(*new_vse) &&
(curr_state->viterbi_state_entries_prunable_length >=
language_model_viterbi_list_max_num_prunable) &&
new_vse->cost >= curr_state->viterbi_state_entries_prunable_max_cost) {
if (language_model_debug_level > 1) {
tprintf("Discarded ViterbiEntry with high cost %g max cost %g\n",
new_vse->cost,
curr_state->viterbi_state_entries_prunable_max_cost);
}
delete new_vse;
return false;
}
// Update best choice if needed.
if (word_end) {
UpdateBestChoice(new_vse, pain_points, word_res,
best_choice_bundle, blamer_bundle);
// Discard the entry if UpdateBestChoice() found flaws in it.
if (new_vse->cost >= WERD_CHOICE::kBadRating &&
new_vse != best_choice_bundle->best_vse) {
if (language_model_debug_level > 1) {
tprintf("Discarded ViterbiEntry with high cost %g\n", new_vse->cost);
}
delete new_vse;
return false;
}
}
// Add the new ViterbiStateEntry and to curr_state->viterbi_state_entries.
curr_state->viterbi_state_entries.add_sorted(ViterbiStateEntry::Compare,
false, new_vse);
curr_state->viterbi_state_entries_length++;
if (PrunablePath(*new_vse)) {
curr_state->viterbi_state_entries_prunable_length++;
}
// Update lms->viterbi_state_entries_prunable_max_cost and clear
// top_choice_flags of entries with ratings_sum than new_vse->ratings_sum.
if ((curr_state->viterbi_state_entries_prunable_length >=
language_model_viterbi_list_max_num_prunable) ||
new_vse->top_choice_flags) {
ASSERT_HOST(!curr_state->viterbi_state_entries.empty());
int prunable_counter = language_model_viterbi_list_max_num_prunable;
vit.set_to_list(&(curr_state->viterbi_state_entries));
for (vit.mark_cycle_pt(); !vit.cycled_list(); vit.forward()) {
ViterbiStateEntry *curr_vse = vit.data();
// Clear the appropriate top choice flags of the entries in the
// list that have cost higher thank new_entry->cost
// (since they will not be top choices any more).
if (curr_vse->top_choice_flags && curr_vse != new_vse &&
curr_vse->cost > new_vse->cost) {
curr_vse->top_choice_flags &= ~(new_vse->top_choice_flags);
}
if (prunable_counter > 0 && PrunablePath(*curr_vse)) --prunable_counter;
// Update curr_state->viterbi_state_entries_prunable_max_cost.
if (prunable_counter == 0) {
curr_state->viterbi_state_entries_prunable_max_cost = vit.data()->cost;
if (language_model_debug_level > 1) {
tprintf("Set viterbi_state_entries_prunable_max_cost to %g\n",
curr_state->viterbi_state_entries_prunable_max_cost);
}
prunable_counter = -1; // stop counting
}
}
}
// Print the newly created ViterbiStateEntry.
if (language_model_debug_level > 2) {
new_vse->Print("New");
if (language_model_debug_level > 5)
curr_state->Print("Updated viterbi list");
}
return true;
}
void LanguageModel::GenerateTopChoiceInfo(ViterbiStateEntry *new_vse,
const ViterbiStateEntry *parent_vse,
LanguageModelState *lms) {
ViterbiStateEntry_IT vit(&(lms->viterbi_state_entries));
for (vit.mark_cycle_pt(); !vit.cycled_list() && new_vse->top_choice_flags &&
new_vse->cost >= vit.data()->cost; vit.forward()) {
// Clear the appropriate flags if the list already contains
// a top choice entry with a lower cost.
new_vse->top_choice_flags &= ~(vit.data()->top_choice_flags);
}
if (language_model_debug_level > 2) {
tprintf("GenerateTopChoiceInfo: top_choice_flags=0x%x\n",
new_vse->top_choice_flags);
}
}
LanguageModelDawgInfo *LanguageModel::GenerateDawgInfo(
bool word_end,
int curr_col, int curr_row,
const BLOB_CHOICE &b,
const ViterbiStateEntry *parent_vse) {
// Initialize active_dawgs from parent_vse if it is not NULL.
// Otherwise use very_beginning_active_dawgs_.
if (parent_vse == NULL) {
dawg_args_->active_dawgs = very_beginning_active_dawgs_;
dawg_args_->permuter = NO_PERM;
} else {
if (parent_vse->dawg_info == NULL) return NULL; // not a dict word path
dawg_args_->active_dawgs = parent_vse->dawg_info->active_dawgs;
dawg_args_->permuter = parent_vse->dawg_info->permuter;
}
// Deal with hyphenated words.
if (word_end && dict_->has_hyphen_end(b.unichar_id(), curr_col == 0)) {
if (language_model_debug_level > 0) tprintf("Hyphenated word found\n");
return new LanguageModelDawgInfo(dawg_args_->active_dawgs,
COMPOUND_PERM);
}
// Deal with compound words.
if (dict_->compound_marker(b.unichar_id()) &&
(parent_vse == NULL || parent_vse->dawg_info->permuter != NUMBER_PERM)) {
if (language_model_debug_level > 0) tprintf("Found compound marker\n");
// Do not allow compound operators at the beginning and end of the word.
// Do not allow more than one compound operator per word.
// Do not allow compounding of words with lengths shorter than
// language_model_min_compound_length
if (parent_vse == NULL || word_end ||
dawg_args_->permuter == COMPOUND_PERM ||
parent_vse->length < language_model_min_compound_length) return NULL;
int i;
// Check a that the path terminated before the current character is a word.
bool has_word_ending = false;
for (i = 0; i < parent_vse->dawg_info->active_dawgs->size(); ++i) {
const DawgPosition &pos = (*parent_vse->dawg_info->active_dawgs)[i];
const Dawg *pdawg = pos.dawg_index < 0
? NULL : dict_->GetDawg(pos.dawg_index);
if (pdawg == NULL || pos.back_to_punc) continue;;
if (pdawg->type() == DAWG_TYPE_WORD && pos.dawg_ref != NO_EDGE &&
pdawg->end_of_word(pos.dawg_ref)) {
has_word_ending = true;
break;
}
}
if (!has_word_ending) return NULL;
if (language_model_debug_level > 0) tprintf("Compound word found\n");
return new LanguageModelDawgInfo(beginning_active_dawgs_, COMPOUND_PERM);
} // done dealing with compound words
LanguageModelDawgInfo *dawg_info = NULL;
// Call LetterIsOkay().
// Use the normalized IDs so that all shapes of ' can be allowed in words
// like don't.
const GenericVector<UNICHAR_ID>& normed_ids =
dict_->getUnicharset().normed_ids(b.unichar_id());
DawgPositionVector tmp_active_dawgs;
for (int i = 0; i < normed_ids.size(); ++i) {
if (language_model_debug_level > 2)
tprintf("Test Letter OK for unichar %d, normed %d\n",
b.unichar_id(), normed_ids[i]);
dict_->LetterIsOkay(dawg_args_, normed_ids[i],
word_end && i == normed_ids.size() - 1);
if (dawg_args_->permuter == NO_PERM) {
break;
} else if (i < normed_ids.size() - 1) {
tmp_active_dawgs = *dawg_args_->updated_dawgs;
dawg_args_->active_dawgs = &tmp_active_dawgs;
}
if (language_model_debug_level > 2)
tprintf("Letter was OK for unichar %d, normed %d\n",
b.unichar_id(), normed_ids[i]);
}
dawg_args_->active_dawgs = NULL;
if (dawg_args_->permuter != NO_PERM) {
dawg_info = new LanguageModelDawgInfo(dawg_args_->updated_dawgs,
dawg_args_->permuter);
} else if (language_model_debug_level > 3) {
tprintf("Letter %s not OK!\n",
dict_->getUnicharset().id_to_unichar(b.unichar_id()));
}
return dawg_info;
}
LanguageModelNgramInfo *LanguageModel::GenerateNgramInfo(
const char *unichar, float certainty, float denom,
int curr_col, int curr_row, float outline_length,
const ViterbiStateEntry *parent_vse) {
// Initialize parent context.
const char *pcontext_ptr = "";
int pcontext_unichar_step_len = 0;
if (parent_vse == NULL) {
pcontext_ptr = prev_word_str_.string();
pcontext_unichar_step_len = prev_word_unichar_step_len_;
} else {
pcontext_ptr = parent_vse->ngram_info->context.string();
pcontext_unichar_step_len =
parent_vse->ngram_info->context_unichar_step_len;
}
// Compute p(unichar | parent context).
int unichar_step_len = 0;
bool pruned = false;
float ngram_cost;
float ngram_and_classifier_cost =
ComputeNgramCost(unichar, certainty, denom,
pcontext_ptr, &unichar_step_len,
&pruned, &ngram_cost);
// Normalize just the ngram_and_classifier_cost by outline_length.
// The ngram_cost is used by the params_model, so it needs to be left as-is,
// and the params model cost will be normalized by outline_length.
ngram_and_classifier_cost *=
outline_length / language_model_ngram_rating_factor;
// Add the ngram_cost of the parent.
if (parent_vse != NULL) {
ngram_and_classifier_cost +=
parent_vse->ngram_info->ngram_and_classifier_cost;
ngram_cost += parent_vse->ngram_info->ngram_cost;
}
// Shorten parent context string by unichar_step_len unichars.
int num_remove = (unichar_step_len + pcontext_unichar_step_len -
language_model_ngram_order);
if (num_remove > 0) pcontext_unichar_step_len -= num_remove;
while (num_remove > 0 && *pcontext_ptr != '\0') {
pcontext_ptr += UNICHAR::utf8_step(pcontext_ptr);
--num_remove;
}
// Decide whether to prune this ngram path and update changed accordingly.
if (parent_vse != NULL && parent_vse->ngram_info->pruned) pruned = true;
// Construct and return the new LanguageModelNgramInfo.
LanguageModelNgramInfo *ngram_info = new LanguageModelNgramInfo(
pcontext_ptr, pcontext_unichar_step_len, pruned, ngram_cost,
ngram_and_classifier_cost);
ngram_info->context += unichar;
ngram_info->context_unichar_step_len += unichar_step_len;
assert(ngram_info->context_unichar_step_len <= language_model_ngram_order);
return ngram_info;
}
float LanguageModel::ComputeNgramCost(const char *unichar,
float certainty,
float denom,
const char *context,
int *unichar_step_len,
bool *found_small_prob,
float *ngram_cost) {
const char *context_ptr = context;
char *modified_context = NULL;
char *modified_context_end = NULL;
const char *unichar_ptr = unichar;
const char *unichar_end = unichar_ptr + strlen(unichar_ptr);
float prob = 0.0f;
int step = 0;
while (unichar_ptr < unichar_end &&
(step = UNICHAR::utf8_step(unichar_ptr)) > 0) {
if (language_model_debug_level > 1) {
tprintf("prob(%s | %s)=%g\n", unichar_ptr, context_ptr,
dict_->ProbabilityInContext(context_ptr, -1, unichar_ptr, step));
}
prob += dict_->ProbabilityInContext(context_ptr, -1, unichar_ptr, step);
++(*unichar_step_len);
if (language_model_ngram_use_only_first_uft8_step) break;
unichar_ptr += step;
// If there are multiple UTF8 characters present in unichar, context is
// updated to include the previously examined characters from str,
// unless use_only_first_uft8_step is true.
if (unichar_ptr < unichar_end) {
if (modified_context == NULL) {
int context_len = strlen(context);
modified_context =
new char[context_len + strlen(unichar_ptr) + step + 1];
strncpy(modified_context, context, context_len);
modified_context_end = modified_context + context_len;
context_ptr = modified_context;
}
strncpy(modified_context_end, unichar_ptr - step, step);
modified_context_end += step;
*modified_context_end = '\0';
}
}
prob /= static_cast<float>(*unichar_step_len); // normalize
if (prob < language_model_ngram_small_prob) {
if (language_model_debug_level > 0) tprintf("Found small prob %g\n", prob);
*found_small_prob = true;
prob = language_model_ngram_small_prob;
}
*ngram_cost = -1.0*log2(prob);
float ngram_and_classifier_cost =
-1.0*log2(CertaintyScore(certainty)/denom) +
*ngram_cost * language_model_ngram_scale_factor;
if (language_model_debug_level > 1) {
tprintf("-log [ p(%s) * p(%s | %s) ] = -log2(%g*%g) = %g\n", unichar,
unichar, context_ptr, CertaintyScore(certainty)/denom, prob,
ngram_and_classifier_cost);
}
if (modified_context != NULL) delete[] modified_context;
return ngram_and_classifier_cost;
}
float LanguageModel::ComputeDenom(BLOB_CHOICE_LIST *curr_list) {
if (curr_list->empty()) return 1.0f;
float denom = 0.0f;
int len = 0;
BLOB_CHOICE_IT c_it(curr_list);
for (c_it.mark_cycle_pt(); !c_it.cycled_list(); c_it.forward()) {
ASSERT_HOST(c_it.data() != NULL);
++len;
denom += CertaintyScore(c_it.data()->certainty());
}
assert(len != 0);
// The ideal situation would be to have the classifier scores for
// classifying each position as each of the characters in the unicharset.
// Since we can not do this because of speed, we add a very crude estimate
// of what these scores for the "missing" classifications would sum up to.
denom += (dict_->getUnicharset().size() - len) *
CertaintyScore(language_model_ngram_nonmatch_score);
return denom;
}
void LanguageModel::FillConsistencyInfo(
int curr_col,
bool word_end,
BLOB_CHOICE *b,
ViterbiStateEntry *parent_vse,
WERD_RES *word_res,