tesseract  4.1.1
segsearch.cpp
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1 // File: segsearch.cpp
3 // Description: Segmentation search functions.
4 // Author: Daria Antonova
5 //
6 // (C) Copyright 2009, Google Inc.
7 // Licensed under the Apache License, Version 2.0 (the "License");
8 // you may not use this file except in compliance with the License.
9 // You may obtain a copy of the License at
10 // http://www.apache.org/licenses/LICENSE-2.0
11 // Unless required by applicable law or agreed to in writing, software
12 // distributed under the License is distributed on an "AS IS" BASIS,
13 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 // See the License for the specific language governing permissions and
15 // limitations under the License.
16 //
18 
19 #include <cstdint> // for INT32_MAX
20 #include "blamer.h" // for BlamerBundle
21 #include "errcode.h" // for ASSERT_HOST
22 #include "genericvector.h" // for GenericVector
23 #include "lm_pain_points.h" // for LMPainPoints, LM_PPTYPE_SHAPE, LMPainPoi...
24 #include "lm_state.h" // for BestChoiceBundle, ViterbiStateEntry
25 #include "matrix.h" // for MATRIX_COORD, MATRIX
26 #include "pageres.h" // for WERD_RES
27 #include "params.h" // for BoolParam, IntParam, DoubleParam
28 #include "ratngs.h" // for BLOB_CHOICE_LIST, BLOB_CHOICE_IT
29 #include "strngs.h" // for STRING
30 #include "tesscallback.h" // for TessResultCallback2
31 #include "tprintf.h" // for tprintf
32 #include "wordrec.h" // for Wordrec, SegSearchPending (ptr only)
33 
34 namespace tesseract {
35 
36 void Wordrec::DoSegSearch(WERD_RES* word_res) {
37  BestChoiceBundle best_choice_bundle(word_res->ratings->dimension());
38  // Run Segmentation Search.
39  SegSearch(word_res, &best_choice_bundle, nullptr);
40 }
41 
43  BestChoiceBundle* best_choice_bundle,
44  BlamerBundle* blamer_bundle) {
49  // Compute scaling factor that will help us recover blob outline length
50  // from classifier rating and certainty for the blob.
51  float rating_cert_scale = -1.0 * getDict().certainty_scale / rating_scale;
53  InitialSegSearch(word_res, &pain_points, &pending, best_choice_bundle,
54  blamer_bundle);
55 
56  if (!SegSearchDone(0)) { // find a better choice
57  if (chop_enable && word_res->chopped_word != nullptr) {
58  improve_by_chopping(rating_cert_scale, word_res, best_choice_bundle,
59  blamer_bundle, &pain_points, &pending);
60  }
61  if (chop_debug) SEAM::PrintSeams("Final seam list:", word_res->seam_array);
62 
63  if (blamer_bundle != nullptr &&
64  !blamer_bundle->ChoiceIsCorrect(word_res->best_choice)) {
65  blamer_bundle->SetChopperBlame(word_res, wordrec_debug_blamer);
66  }
67  }
68  // Keep trying to find a better path by fixing the "pain points".
69 
70  MATRIX_COORD pain_point;
71  float pain_point_priority;
72  int num_futile_classifications = 0;
73  STRING blamer_debug;
74  while (wordrec_enable_assoc &&
75  (!SegSearchDone(num_futile_classifications) ||
76  (blamer_bundle != nullptr &&
77  blamer_bundle->GuidedSegsearchStillGoing()))) {
78  // Get the next valid "pain point".
79  bool found_nothing = true;
80  LMPainPointsType pp_type;
81  while ((pp_type = pain_points.Deque(&pain_point, &pain_point_priority)) !=
82  LM_PPTYPE_NUM) {
83  if (!pain_point.Valid(*word_res->ratings)) {
84  word_res->ratings->IncreaseBandSize(
85  pain_point.row - pain_point.col + 1);
86  }
87  if (pain_point.Valid(*word_res->ratings) &&
88  !word_res->ratings->Classified(pain_point.col, pain_point.row,
89  getDict().WildcardID())) {
90  found_nothing = false;
91  break;
92  }
93  }
94  if (found_nothing) {
95  if (segsearch_debug_level > 0) tprintf("Pain points queue is empty\n");
96  break;
97  }
98  ProcessSegSearchPainPoint(pain_point_priority, pain_point,
100  &pending, word_res, &pain_points, blamer_bundle);
101 
102  UpdateSegSearchNodes(rating_cert_scale, pain_point.col, &pending,
103  word_res, &pain_points, best_choice_bundle,
104  blamer_bundle);
105  if (!best_choice_bundle->updated) ++num_futile_classifications;
106 
107  if (segsearch_debug_level > 0) {
108  tprintf("num_futile_classifications %d\n", num_futile_classifications);
109  }
110 
111  best_choice_bundle->updated = false; // reset updated
112 
113  // See if it's time to terminate SegSearch or time for starting a guided
114  // search for the true path to find the blame for the incorrect best_choice.
115  if (SegSearchDone(num_futile_classifications) &&
116  blamer_bundle != nullptr &&
117  blamer_bundle->GuidedSegsearchNeeded(word_res->best_choice)) {
118  InitBlamerForSegSearch(word_res, &pain_points, blamer_bundle,
119  &blamer_debug);
120  }
121  } // end while loop exploring alternative paths
122  if (blamer_bundle != nullptr) {
123  blamer_bundle->FinishSegSearch(word_res->best_choice,
124  wordrec_debug_blamer, &blamer_debug);
125  }
126 
127  if (segsearch_debug_level > 0) {
128  tprintf("Done with SegSearch (AcceptableChoiceFound: %d)\n",
129  language_model_->AcceptableChoiceFound());
130  }
131 }
132 
133 // Setup and run just the initial segsearch on an established matrix,
134 // without doing any additional chopping or joining.
135 // (Internal factored version that can be used as part of the main SegSearch.)
136 void Wordrec::InitialSegSearch(WERD_RES* word_res, LMPainPoints* pain_points,
138  BestChoiceBundle* best_choice_bundle,
139  BlamerBundle* blamer_bundle) {
140  if (segsearch_debug_level > 0) {
141  tprintf("Starting SegSearch on ratings matrix%s:\n",
142  wordrec_enable_assoc ? " (with assoc)" : "");
143  word_res->ratings->print(getDict().getUnicharset());
144  }
145 
146  pain_points->GenerateInitial(word_res);
147 
148  // Compute scaling factor that will help us recover blob outline length
149  // from classifier rating and certainty for the blob.
150  float rating_cert_scale = -1.0 * getDict().certainty_scale / rating_scale;
151 
154  segsearch_max_char_wh_ratio, rating_cert_scale);
155 
156  // Initialize blamer-related information: map character boxes recorded in
157  // blamer_bundle->norm_truth_word to the corresponding i,j indices in the
158  // ratings matrix. We expect this step to succeed, since when running the
159  // chopper we checked that the correct chops are present.
160  if (blamer_bundle != nullptr) {
161  blamer_bundle->SetupCorrectSegmentation(word_res->chopped_word,
163  }
164 
165  // pending[col] tells whether there is update work to do to combine
166  // best_choice_bundle->beam[col - 1] with some BLOB_CHOICEs in matrix[col, *].
167  // As the language model state is updated, pending entries are modified to
168  // minimize duplication of work. It is important that during the update the
169  // children are considered in the non-decreasing order of their column, since
170  // this guarantees that all the parents would be up to date before an update
171  // of a child is done.
172  pending->init_to_size(word_res->ratings->dimension(), SegSearchPending());
173 
174  // Search the ratings matrix for the initial best path.
175  (*pending)[0].SetColumnClassified();
176  UpdateSegSearchNodes(rating_cert_scale, 0, pending, word_res,
177  pain_points, best_choice_bundle, blamer_bundle);
178 }
179 
181  float rating_cert_scale,
182  int starting_col,
184  WERD_RES *word_res,
185  LMPainPoints *pain_points,
186  BestChoiceBundle *best_choice_bundle,
187  BlamerBundle *blamer_bundle) {
188  MATRIX *ratings = word_res->ratings;
189  ASSERT_HOST(ratings->dimension() == pending->size());
190  ASSERT_HOST(ratings->dimension() == best_choice_bundle->beam.size());
191  for (int col = starting_col; col < ratings->dimension(); ++col) {
192  if (!(*pending)[col].WorkToDo()) continue;
193  int first_row = col;
194  int last_row = std::min(ratings->dimension() - 1,
195  col + ratings->bandwidth() - 1);
196  if ((*pending)[col].SingleRow() >= 0) {
197  first_row = last_row = (*pending)[col].SingleRow();
198  }
199  if (segsearch_debug_level > 0) {
200  tprintf("\n\nUpdateSegSearchNodes: col=%d, rows=[%d,%d], alljust=%d\n",
201  col, first_row, last_row,
202  (*pending)[col].IsRowJustClassified(INT32_MAX));
203  }
204  // Iterate over the pending list for this column.
205  for (int row = first_row; row <= last_row; ++row) {
206  // Update language model state of this child+parent pair.
207  BLOB_CHOICE_LIST *current_node = ratings->get(col, row);
208  LanguageModelState *parent_node =
209  col == 0 ? nullptr : best_choice_bundle->beam[col - 1];
210  if (current_node != nullptr &&
211  language_model_->UpdateState((*pending)[col].IsRowJustClassified(row),
212  col, row, current_node, parent_node,
213  pain_points, word_res,
214  best_choice_bundle, blamer_bundle) &&
215  row + 1 < ratings->dimension()) {
216  // Since the language model state of this entry changed, process all
217  // the child column.
218  (*pending)[row + 1].RevisitWholeColumn();
219  if (segsearch_debug_level > 0) {
220  tprintf("Added child col=%d to pending\n", row + 1);
221  }
222  } // end if UpdateState.
223  } // end for row.
224  } // end for col.
225  if (best_choice_bundle->best_vse != nullptr) {
226  ASSERT_HOST(word_res->StatesAllValid());
227  if (best_choice_bundle->best_vse->updated) {
228  pain_points->GenerateFromPath(rating_cert_scale,
229  best_choice_bundle->best_vse, word_res);
230  if (!best_choice_bundle->fixpt.empty()) {
231  pain_points->GenerateFromAmbigs(best_choice_bundle->fixpt,
232  best_choice_bundle->best_vse, word_res);
233  }
234  }
235  }
236  // The segsearch is completed. Reset all updated flags on all VSEs and reset
237  // all pendings.
238  for (int col = 0; col < pending->size(); ++col) {
239  (*pending)[col].Clear();
240  ViterbiStateEntry_IT
241  vse_it(&best_choice_bundle->beam[col]->viterbi_state_entries);
242  for (vse_it.mark_cycle_pt(); !vse_it.cycled_list(); vse_it.forward()) {
243  vse_it.data()->updated = false;
244  }
245  }
246 }
247 
249  float pain_point_priority,
250  const MATRIX_COORD &pain_point, const char* pain_point_type,
251  GenericVector<SegSearchPending>* pending, WERD_RES *word_res,
252  LMPainPoints *pain_points, BlamerBundle *blamer_bundle) {
253  if (segsearch_debug_level > 0) {
254  tprintf("Classifying pain point %s priority=%.4f, col=%d, row=%d\n",
255  pain_point_type, pain_point_priority,
256  pain_point.col, pain_point.row);
257  }
258  ASSERT_HOST(pain_points != nullptr);
259  MATRIX *ratings = word_res->ratings;
260  // Classify blob [pain_point.col pain_point.row]
261  if (!pain_point.Valid(*ratings)) {
262  ratings->IncreaseBandSize(pain_point.row + 1 - pain_point.col);
263  }
264  ASSERT_HOST(pain_point.Valid(*ratings));
265  BLOB_CHOICE_LIST *classified = classify_piece(word_res->seam_array,
266  pain_point.col, pain_point.row,
267  pain_point_type,
268  word_res->chopped_word,
269  blamer_bundle);
270  BLOB_CHOICE_LIST *lst = ratings->get(pain_point.col, pain_point.row);
271  if (lst == nullptr) {
272  ratings->put(pain_point.col, pain_point.row, classified);
273  } else {
274  // We can not delete old BLOB_CHOICEs, since they might contain
275  // ViterbiStateEntries that are parents of other "active" entries.
276  // Thus if the matrix cell already contains classifications we add
277  // the new ones to the beginning of the list.
278  BLOB_CHOICE_IT it(lst);
279  it.add_list_before(classified);
280  delete classified; // safe to delete, since empty after add_list_before()
281  classified = nullptr;
282  }
283 
284  if (segsearch_debug_level > 0) {
285  print_ratings_list("Updated ratings matrix with a new entry:",
286  ratings->get(pain_point.col, pain_point.row),
287  getDict().getUnicharset());
288  ratings->print(getDict().getUnicharset());
289  }
290 
291  // Insert initial "pain points" to join the newly classified blob
292  // with its left and right neighbors.
293  if (classified != nullptr && !classified->empty()) {
294  if (pain_point.col > 0) {
295  pain_points->GeneratePainPoint(
296  pain_point.col - 1, pain_point.row, LM_PPTYPE_SHAPE, 0.0,
297  true, segsearch_max_char_wh_ratio, word_res);
298  }
299  if (pain_point.row + 1 < ratings->dimension()) {
300  pain_points->GeneratePainPoint(
301  pain_point.col, pain_point.row + 1, LM_PPTYPE_SHAPE, 0.0,
302  true, segsearch_max_char_wh_ratio, word_res);
303  }
304  }
305  (*pending)[pain_point.col].SetBlobClassified(pain_point.row);
306 }
307 
308 // Resets enough of the results so that the Viterbi search is re-run.
309 // Needed when the n-gram model is enabled, as the multi-length comparison
310 // implementation will re-value existing paths to worse values.
312  BestChoiceBundle* best_choice_bundle,
314  // TODO(rays) More refactoring required here.
315  // Delete existing viterbi states.
316  for (int col = 0; col < best_choice_bundle->beam.size(); ++col) {
317  best_choice_bundle->beam[col]->Clear();
318  }
319  // Reset best_choice_bundle.
320  word_res->ClearWordChoices();
321  best_choice_bundle->best_vse = nullptr;
322  // Clear out all existing pendings and add a new one for the first column.
323  (*pending)[0].SetColumnClassified();
324  for (int i = 1; i < pending->size(); ++i)
325  (*pending)[i].Clear();
326 }
327 
329  LMPainPoints *pain_points,
330  BlamerBundle *blamer_bundle,
331  STRING *blamer_debug) {
332  pain_points->Clear(); // Clear pain points heap.
334  pain_points, &LMPainPoints::GenerateForBlamer,
335  static_cast<double>(segsearch_max_char_wh_ratio), word_res);
336  blamer_bundle->InitForSegSearch(word_res->best_choice, word_res->ratings,
337  getDict().WildcardID(), wordrec_debug_blamer,
338  blamer_debug, pp_cb);
339  delete pp_cb;
340 }
341 
342 } // namespace tesseract
tesseract::LMPainPoints::GeneratePainPoint
bool GeneratePainPoint(int col, int row, LMPainPointsType pp_type, float special_priority, bool ok_to_extend, float max_char_wh_ratio, WERD_RES *word_res)
Definition: lm_pain_points.cpp:148
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void init_to_size(int size, const T &t)
Definition: genericvector.h:744
tesseract::Wordrec::prev_word_best_choice_
WERD_CHOICE * prev_word_best_choice_
Definition: wordrec.h:476
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virtual Dict & getDict()
Definition: classify.h:107
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void FinishSegSearch(const WERD_CHOICE *best_choice, bool debug, STRING *debug_str)
Definition: blamer.cpp:519
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bool updated
set to true if the entry has just been created/updated
Definition: lm_state.h:194
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Definition: matrix.h:636
tesseract::Wordrec::chop_debug
int chop_debug
Definition: wordrec.h:204
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int bandwidth() const
Definition: matrix.h:538
tesseract::Wordrec::SegSearchDone
bool SegSearchDone(int num_futile_classifications)
Definition: wordrec.h:486
MATRIX_COORD
Definition: matrix.h:608
tesseract::Wordrec::UpdateSegSearchNodes
void UpdateSegSearchNodes(float rating_cert_scale, int starting_col, GenericVector< SegSearchPending > *pending, WERD_RES *word_res, LMPainPoints *pain_points, BestChoiceBundle *best_choice_bundle, BlamerBundle *blamer_bundle)
Definition: segsearch.cpp:180
tesseract::LMPainPoints::GenerateFromPath
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Definition: lm_pain_points.cpp:70
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void SetupCorrectSegmentation(const TWERD *word, bool debug)
Definition: blamer.cpp:415
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GenericVector< SEAM * > seam_array
Definition: pageres.h:214
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std::unique_ptr< LanguageModel > language_model_
Definition: wordrec.h:471
tesseract::Wordrec::classify_piece
virtual BLOB_CHOICE_LIST * classify_piece(const GenericVector< SEAM * > &seams, int16_t start, int16_t end, const char *description, TWERD *word, BlamerBundle *blamer_bundle)
Definition: pieces.cpp:56
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Bundle together all the things pertaining to the best choice/state.
Definition: lm_state.h:222
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bool Valid(const MATRIX &m) const
Definition: matrix.h:618
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Definition: seam.cpp:167
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void InitialSegSearch(WERD_RES *word_res, LMPainPoints *pain_points, GenericVector< SegSearchPending > *pending, BestChoiceBundle *best_choice_bundle, BlamerBundle *blamer_bundle)
Definition: segsearch.cpp:136
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Definition: blamer.cpp:514
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Definition: lm_pain_points.h:103
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Definition: matrix.h:637
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Definition: wordrec.h:225
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Definition: lm_pain_points.h:66
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Definition: wordrec.h:231
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Definition: matrix.cpp:49
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Definition: classify.h:472
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Definition: wordrec.h:233
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Definition: tprintf.cpp:35
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Definition: errcode.h:88
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Definition: blamer.h:45
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Definition: matrix.cpp:36
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Definition: pageres.cpp:1129
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Definition: blamer.h:102
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Definition: wordrec.h:205
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Definition: wordrec.h:112
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_ConstTessMemberResultCallback_5_0< false, R, T1, P1, P2, P3, P4, P5 >::base * NewPermanentTessCallback(const T1 *obj, R(T2::*member)(P1, P2, P3, P4, P5) const, typename Identity< P1 >::type p1, typename Identity< P2 >::type p2, typename Identity< P3 >::type p3, typename Identity< P4 >::type p4, typename Identity< P5 >::type p5)
Definition: tesscallback.h:258
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void SegSearch(WERD_RES *word_res, BestChoiceBundle *best_choice_bundle, BlamerBundle *blamer_bundle)
Definition: segsearch.cpp:42
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@ LM_PPTYPE_NUM
Definition: lm_pain_points.h:47
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Definition: wordrec.h:198
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Definition: lm_pain_points.h:57
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Definition: lm_state.h:238
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Definition: blamer.cpp:318
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Definition: pageres.h:166
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Definition: wordrec.h:235
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Definition: matrix.h:536
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Definition: pageres.h:237
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Definition: lm_pain_points.h:45
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Definition: matrix.h:578
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Definition: pageres.h:212
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Definition: segsearch.cpp:36
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Definition: pageres.cpp:458
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Definition: blamer.cpp:484
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Struct to store information maintained by various language model components.
Definition: lm_state.h:200
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Definition: lm_pain_points.h:41
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Definition: ratngs.cpp:837
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Definition: lm_pain_points.cpp:39
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Definition: wordrec.h:239
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void ProcessSegSearchPainPoint(float pain_point_priority, const MATRIX_COORD &pain_point, const char *pain_point_type, GenericVector< SegSearchPending > *pending, WERD_RES *word_res, LMPainPoints *pain_points, BlamerBundle *blamer_bundle)
Definition: segsearch.cpp:248
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Definition: blamer.cpp:119
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Definition: blamer.cpp:471
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Definition: dict.h:627
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Definition: chopper.cpp:454
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void InitBlamerForSegSearch(WERD_RES *word_res, LMPainPoints *pain_points, BlamerBundle *blamer_bundle, STRING *blamer_debug)
Definition: segsearch.cpp:328
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GENERIC_2D_ARRAY::get
T get(ICOORD pos) const
Definition: matrix.h:231
tesseract::Wordrec::ResetNGramSearch
void ResetNGramSearch(WERD_RES *word_res, BestChoiceBundle *best_choice_bundle, GenericVector< SegSearchPending > *pending)
Definition: segsearch.cpp:311
tesseract::LMPainPoints::Clear
void Clear()
Definition: lm_pain_points.h:86
lm_pain_points.h
STRING
Definition: strngs.h:45
params.h
GenericVector::empty
bool empty() const
Definition: genericvector.h:91
tesscallback.h
pageres.h