/****************************************************************************** ** Filename: adaptmatch.c ** Purpose: High level adaptive matcher. ** Author: Dan Johnson ** History: Mon Mar 11 10:00:10 1991, DSJ, Created. ** ** (c) Copyright Hewlett-Packard Company, 1988. ** 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 Files and Type Defines -----------------------------------------------------------------------------*/ #ifdef HAVE_CONFIG_H #include "config_auto.h" #endif #include #include "shapeclassifier.h" #include "ambigs.h" #include "blobclass.h" #include "blobs.h" #include "callcpp.h" #include "classify.h" #include "const.h" #include "dict.h" #include "efio.h" #include "emalloc.h" #include "featdefs.h" #include "float2int.h" #include "genericvector.h" #include "globals.h" #include "helpers.h" #include "intfx.h" #include "intproto.h" #include "mfoutline.h" #include "ndminx.h" #include "normfeat.h" #include "normmatch.h" #include "outfeat.h" #include "pageres.h" #include "params.h" #include "picofeat.h" #include "shapetable.h" #include "tessclassifier.h" #include "trainingsample.h" #include "unicharset.h" #include "werd.h" #include #include #include #include #ifdef __UNIX__ #include #endif #define ADAPT_TEMPLATE_SUFFIX ".a" #define MAX_MATCHES 10 #define UNLIKELY_NUM_FEAT 200 #define NO_DEBUG 0 #define MAX_ADAPTABLE_WERD_SIZE 40 #define ADAPTABLE_WERD_ADJUSTMENT (0.05) #define Y_DIM_OFFSET (Y_SHIFT - BASELINE_Y_SHIFT) #define WORST_POSSIBLE_RATING (0.0f) using tesseract::UnicharRating; using tesseract::ScoredFont; struct ADAPT_RESULTS { inT32 BlobLength; bool HasNonfragment; UNICHAR_ID best_unichar_id; int best_match_index; FLOAT32 best_rating; GenericVector match; GenericVector CPResults; /// Initializes data members to the default values. Sets the initial /// rating of each class to be the worst possible rating (1.0). inline void Initialize() { BlobLength = MAX_INT32; HasNonfragment = false; ComputeBest(); } // Computes best_unichar_id, best_match_index and best_rating. void ComputeBest() { best_unichar_id = INVALID_UNICHAR_ID; best_match_index = -1; best_rating = WORST_POSSIBLE_RATING; for (int i = 0; i < match.size(); ++i) { if (match[i].rating > best_rating) { best_rating = match[i].rating; best_unichar_id = match[i].unichar_id; best_match_index = i; } } } }; struct PROTO_KEY { ADAPT_TEMPLATES Templates; CLASS_ID ClassId; int ConfigId; }; /*----------------------------------------------------------------------------- Private Macros -----------------------------------------------------------------------------*/ inline bool MarginalMatch(float confidence, float matcher_great_threshold) { return (1.0f - confidence) > matcher_great_threshold; } /*----------------------------------------------------------------------------- Private Function Prototypes -----------------------------------------------------------------------------*/ // Returns the index of the given id in results, if present, or the size of the // vector (index it will go at) if not present. static int FindScoredUnichar(UNICHAR_ID id, const ADAPT_RESULTS& results) { for (int i = 0; i < results.match.size(); i++) { if (results.match[i].unichar_id == id) return i; } return results.match.size(); } // Returns the current rating for a unichar id if we have rated it, defaulting // to WORST_POSSIBLE_RATING. static float ScoredUnichar(UNICHAR_ID id, const ADAPT_RESULTS& results) { int index = FindScoredUnichar(id, results); if (index >= results.match.size()) return WORST_POSSIBLE_RATING; return results.match[index].rating; } void InitMatcherRatings(register FLOAT32 *Rating); int MakeTempProtoPerm(void *item1, void *item2); void SetAdaptiveThreshold(FLOAT32 Threshold); /*----------------------------------------------------------------------------- Public Code -----------------------------------------------------------------------------*/ /*---------------------------------------------------------------------------*/ namespace tesseract { /** * This routine calls the adaptive matcher * which returns (in an array) the class id of each * class matched. * * It also returns the number of classes matched. * For each class matched it places the best rating * found for that class into the Ratings array. * * Bad matches are then removed so that they don't * need to be sorted. The remaining good matches are * then sorted and converted to choices. * * This routine also performs some simple speckle * filtering. * * @note Exceptions: none * @note History: Mon Mar 11 10:00:58 1991, DSJ, Created. * * @param Blob blob to be classified * @param[out] Choices List of choices found by adaptive matcher. * filled on return with the choices found by the * class pruner and the ratings therefrom. Also * contains the detailed results of the integer matcher. * */ void Classify::AdaptiveClassifier(TBLOB *Blob, BLOB_CHOICE_LIST *Choices) { assert(Choices != NULL); ADAPT_RESULTS *Results = new ADAPT_RESULTS; Results->Initialize(); ASSERT_HOST(AdaptedTemplates != NULL); DoAdaptiveMatch(Blob, Results); RemoveBadMatches(Results); Results->match.sort(&UnicharRating::SortDescendingRating); RemoveExtraPuncs(Results); Results->ComputeBest(); ConvertMatchesToChoices(Blob->denorm(), Blob->bounding_box(), Results, Choices); // TODO(rays) Move to before ConvertMatchesToChoices! if (LargeSpeckle(*Blob) || Choices->length() == 0) AddLargeSpeckleTo(Results->BlobLength, Choices); if (matcher_debug_level >= 1) { tprintf("AD Matches = "); PrintAdaptiveMatchResults(*Results); } #ifndef GRAPHICS_DISABLED if (classify_enable_adaptive_debugger) DebugAdaptiveClassifier(Blob, Results); #endif delete Results; } /* AdaptiveClassifier */ // If *win is NULL, sets it to a new ScrollView() object with title msg. // Clears the window and draws baselines. void Classify::RefreshDebugWindow(ScrollView **win, const char *msg, int y_offset, const TBOX &wbox) { #ifndef GRAPHICS_DISABLED const int kSampleSpaceWidth = 500; if (*win == NULL) { *win = new ScrollView(msg, 100, y_offset, kSampleSpaceWidth * 2, 200, kSampleSpaceWidth * 2, 200, true); } (*win)->Clear(); (*win)->Pen(64, 64, 64); (*win)->Line(-kSampleSpaceWidth, kBlnBaselineOffset, kSampleSpaceWidth, kBlnBaselineOffset); (*win)->Line(-kSampleSpaceWidth, kBlnXHeight + kBlnBaselineOffset, kSampleSpaceWidth, kBlnXHeight + kBlnBaselineOffset); (*win)->ZoomToRectangle(wbox.left(), wbox.top(), wbox.right(), wbox.bottom()); #endif // GRAPHICS_DISABLED } // Learns the given word using its chopped_word, seam_array, denorm, // box_word, best_state, and correct_text to learn both correctly and // incorrectly segmented blobs. If fontname is not NULL, then LearnBlob // is called and the data will be saved in an internal buffer. // Otherwise AdaptToBlob is called for adaption within a document. void Classify::LearnWord(const char* fontname, WERD_RES* word) { int word_len = word->correct_text.size(); if (word_len == 0) return; float* thresholds = NULL; if (fontname == NULL) { // Adaption mode. if (!EnableLearning || word->best_choice == NULL) return; // Can't or won't adapt. if (classify_learning_debug_level >= 1) tprintf("\n\nAdapting to word = %s\n", word->best_choice->debug_string().string()); thresholds = new float[word_len]; word->ComputeAdaptionThresholds(certainty_scale, matcher_perfect_threshold, matcher_good_threshold, matcher_rating_margin, thresholds); } int start_blob = 0; #ifndef GRAPHICS_DISABLED if (classify_debug_character_fragments) { if (learn_fragmented_word_debug_win_ != NULL) { window_wait(learn_fragmented_word_debug_win_); } RefreshDebugWindow(&learn_fragments_debug_win_, "LearnPieces", 400, word->chopped_word->bounding_box()); RefreshDebugWindow(&learn_fragmented_word_debug_win_, "LearnWord", 200, word->chopped_word->bounding_box()); word->chopped_word->plot(learn_fragmented_word_debug_win_); ScrollView::Update(); } #endif // GRAPHICS_DISABLED for (int ch = 0; ch < word_len; ++ch) { if (classify_debug_character_fragments) { tprintf("\nLearning %s\n", word->correct_text[ch].string()); } if (word->correct_text[ch].length() > 0) { float threshold = thresholds != NULL ? thresholds[ch] : 0.0f; LearnPieces(fontname, start_blob, word->best_state[ch], threshold, CST_WHOLE, word->correct_text[ch].string(), word); if (word->best_state[ch] > 1 && !disable_character_fragments) { // Check that the character breaks into meaningful fragments // that each match a whole character with at least // classify_character_fragments_garbage_certainty_threshold bool garbage = false; int frag; for (frag = 0; frag < word->best_state[ch]; ++frag) { TBLOB* frag_blob = word->chopped_word->blobs[start_blob + frag]; if (classify_character_fragments_garbage_certainty_threshold < 0) { garbage |= LooksLikeGarbage(frag_blob); } } // Learn the fragments. if (!garbage) { bool pieces_all_natural = word->PiecesAllNatural(start_blob, word->best_state[ch]); if (pieces_all_natural || !prioritize_division) { for (frag = 0; frag < word->best_state[ch]; ++frag) { GenericVector tokens; word->correct_text[ch].split(' ', &tokens); tokens[0] = CHAR_FRAGMENT::to_string( tokens[0].string(), frag, word->best_state[ch], pieces_all_natural); STRING full_string; for (int i = 0; i < tokens.size(); i++) { full_string += tokens[i]; if (i != tokens.size() - 1) full_string += ' '; } LearnPieces(fontname, start_blob + frag, 1, threshold, CST_FRAGMENT, full_string.string(), word); } } } } // TODO(rays): re-enable this part of the code when we switch to the // new classifier that needs to see examples of garbage. /* if (word->best_state[ch] > 1) { // If the next blob is good, make junk with the rightmost fragment. if (ch + 1 < word_len && word->correct_text[ch + 1].length() > 0) { LearnPieces(fontname, start_blob + word->best_state[ch] - 1, word->best_state[ch + 1] + 1, threshold, CST_IMPROPER, INVALID_UNICHAR, word); } // If the previous blob is good, make junk with the leftmost fragment. if (ch > 0 && word->correct_text[ch - 1].length() > 0) { LearnPieces(fontname, start_blob - word->best_state[ch - 1], word->best_state[ch - 1] + 1, threshold, CST_IMPROPER, INVALID_UNICHAR, word); } } // If the next blob is good, make a join with it. if (ch + 1 < word_len && word->correct_text[ch + 1].length() > 0) { STRING joined_text = word->correct_text[ch]; joined_text += word->correct_text[ch + 1]; LearnPieces(fontname, start_blob, word->best_state[ch] + word->best_state[ch + 1], threshold, CST_NGRAM, joined_text.string(), word); } */ } start_blob += word->best_state[ch]; } delete [] thresholds; } // LearnWord. // Builds a blob of length fragments, from the word, starting at start, // and then learns it, as having the given correct_text. // If fontname is not NULL, then LearnBlob is called and the data will be // saved in an internal buffer for static training. // Otherwise AdaptToBlob is called for adaption within a document. // threshold is a magic number required by AdaptToChar and generated by // ComputeAdaptionThresholds. // Although it can be partly inferred from the string, segmentation is // provided to explicitly clarify the character segmentation. void Classify::LearnPieces(const char* fontname, int start, int length, float threshold, CharSegmentationType segmentation, const char* correct_text, WERD_RES* word) { // TODO(daria) Remove/modify this if/when we want // to train and/or adapt to n-grams. if (segmentation != CST_WHOLE && (segmentation != CST_FRAGMENT || disable_character_fragments)) return; if (length > 1) { SEAM::JoinPieces(word->seam_array, word->chopped_word->blobs, start, start + length - 1); } TBLOB* blob = word->chopped_word->blobs[start]; // Rotate the blob if needed for classification. TBLOB* rotated_blob = blob->ClassifyNormalizeIfNeeded(); if (rotated_blob == NULL) rotated_blob = blob; #ifndef GRAPHICS_DISABLED // Draw debug windows showing the blob that is being learned if needed. if (strcmp(classify_learn_debug_str.string(), correct_text) == 0) { RefreshDebugWindow(&learn_debug_win_, "LearnPieces", 600, word->chopped_word->bounding_box()); rotated_blob->plot(learn_debug_win_, ScrollView::GREEN, ScrollView::BROWN); learn_debug_win_->Update(); window_wait(learn_debug_win_); } if (classify_debug_character_fragments && segmentation == CST_FRAGMENT) { ASSERT_HOST(learn_fragments_debug_win_ != NULL); // set up in LearnWord blob->plot(learn_fragments_debug_win_, ScrollView::BLUE, ScrollView::BROWN); learn_fragments_debug_win_->Update(); } #endif // GRAPHICS_DISABLED if (fontname != NULL) { classify_norm_method.set_value(character); // force char norm spc 30/11/93 tess_bn_matching.set_value(false); // turn it off tess_cn_matching.set_value(false); DENORM bl_denorm, cn_denorm; INT_FX_RESULT_STRUCT fx_info; SetupBLCNDenorms(*rotated_blob, classify_nonlinear_norm, &bl_denorm, &cn_denorm, &fx_info); LearnBlob(fontname, rotated_blob, cn_denorm, fx_info, correct_text); } else if (unicharset.contains_unichar(correct_text)) { UNICHAR_ID class_id = unicharset.unichar_to_id(correct_text); int font_id = word->fontinfo != NULL ? fontinfo_table_.get_id(*word->fontinfo) : 0; if (classify_learning_debug_level >= 1) tprintf("Adapting to char = %s, thr= %g font_id= %d\n", unicharset.id_to_unichar(class_id), threshold, font_id); // If filename is not NULL we are doing recognition // (as opposed to training), so we must have already set word fonts. AdaptToChar(rotated_blob, class_id, font_id, threshold, AdaptedTemplates); if (BackupAdaptedTemplates != NULL) { // Adapt the backup templates too. They will be used if the primary gets // too full. AdaptToChar(rotated_blob, class_id, font_id, threshold, BackupAdaptedTemplates); } } else if (classify_debug_level >= 1) { tprintf("Can't adapt to %s not in unicharset\n", correct_text); } if (rotated_blob != blob) { delete rotated_blob; } SEAM::BreakPieces(word->seam_array, word->chopped_word->blobs, start, start + length - 1); } // LearnPieces. /*---------------------------------------------------------------------------*/ /** * This routine performs cleanup operations * on the adaptive classifier. It should be called * before the program is terminated. Its main function * is to save the adapted templates to a file. * * Globals: * - #AdaptedTemplates current set of adapted templates * - #classify_save_adapted_templates TRUE if templates should be saved * - #classify_enable_adaptive_matcher TRUE if adaptive matcher is enabled * * @note Exceptions: none * @note History: Tue Mar 19 14:37:06 1991, DSJ, Created. */ void Classify::EndAdaptiveClassifier() { STRING Filename; FILE *File; if (AdaptedTemplates != NULL && classify_enable_adaptive_matcher && classify_save_adapted_templates) { Filename = imagefile + ADAPT_TEMPLATE_SUFFIX; File = fopen (Filename.string(), "wb"); if (File == NULL) cprintf ("Unable to save adapted templates to %s!\n", Filename.string()); else { cprintf ("\nSaving adapted templates to %s ...", Filename.string()); fflush(stdout); WriteAdaptedTemplates(File, AdaptedTemplates); cprintf ("\n"); fclose(File); } } if (AdaptedTemplates != NULL) { free_adapted_templates(AdaptedTemplates); AdaptedTemplates = NULL; } if (BackupAdaptedTemplates != NULL) { free_adapted_templates(BackupAdaptedTemplates); BackupAdaptedTemplates = NULL; } if (PreTrainedTemplates != NULL) { free_int_templates(PreTrainedTemplates); PreTrainedTemplates = NULL; } getDict().EndDangerousAmbigs(); FreeNormProtos(); if (AllProtosOn != NULL) { FreeBitVector(AllProtosOn); FreeBitVector(AllConfigsOn); FreeBitVector(AllConfigsOff); FreeBitVector(TempProtoMask); AllProtosOn = NULL; AllConfigsOn = NULL; AllConfigsOff = NULL; TempProtoMask = NULL; } delete shape_table_; shape_table_ = NULL; if (static_classifier_ != NULL) { delete static_classifier_; static_classifier_ = NULL; } } /* EndAdaptiveClassifier */ /*---------------------------------------------------------------------------*/ /** * This routine reads in the training * information needed by the adaptive classifier * and saves it into global variables. * Parameters: * load_pre_trained_templates Indicates whether the pre-trained * templates (inttemp, normproto and pffmtable components) * should be loaded. Should only be set to true if the * necessary classifier components are present in the * [lang].traineddata file. * Globals: * BuiltInTemplatesFile file to get built-in temps from * BuiltInCutoffsFile file to get avg. feat per class from * classify_use_pre_adapted_templates * enables use of pre-adapted templates * @note History: Mon Mar 11 12:49:34 1991, DSJ, Created. */ void Classify::InitAdaptiveClassifier(bool load_pre_trained_templates) { if (!classify_enable_adaptive_matcher) return; if (AllProtosOn != NULL) EndAdaptiveClassifier(); // Don't leak with multiple inits. // If there is no language_data_path_prefix, the classifier will be // adaptive only. if (language_data_path_prefix.length() > 0 && load_pre_trained_templates) { ASSERT_HOST(tessdata_manager.SeekToStart(TESSDATA_INTTEMP)); PreTrainedTemplates = ReadIntTemplates(tessdata_manager.GetDataFilePtr()); if (tessdata_manager.DebugLevel() > 0) tprintf("Loaded inttemp\n"); if (tessdata_manager.SeekToStart(TESSDATA_SHAPE_TABLE)) { shape_table_ = new ShapeTable(unicharset); if (!shape_table_->DeSerialize(tessdata_manager.swap(), tessdata_manager.GetDataFilePtr())) { tprintf("Error loading shape table!\n"); delete shape_table_; shape_table_ = NULL; } else if (tessdata_manager.DebugLevel() > 0) { tprintf("Successfully loaded shape table!\n"); } } ASSERT_HOST(tessdata_manager.SeekToStart(TESSDATA_PFFMTABLE)); ReadNewCutoffs(tessdata_manager.GetDataFilePtr(), tessdata_manager.swap(), tessdata_manager.GetEndOffset(TESSDATA_PFFMTABLE), CharNormCutoffs); if (tessdata_manager.DebugLevel() > 0) tprintf("Loaded pffmtable\n"); ASSERT_HOST(tessdata_manager.SeekToStart(TESSDATA_NORMPROTO)); NormProtos = ReadNormProtos(tessdata_manager.GetDataFilePtr(), tessdata_manager.GetEndOffset(TESSDATA_NORMPROTO)); if (tessdata_manager.DebugLevel() > 0) tprintf("Loaded normproto\n"); static_classifier_ = new TessClassifier(false, this); } im_.Init(&classify_debug_level); InitIntegerFX(); AllProtosOn = NewBitVector(MAX_NUM_PROTOS); AllConfigsOn = NewBitVector(MAX_NUM_CONFIGS); AllConfigsOff = NewBitVector(MAX_NUM_CONFIGS); TempProtoMask = NewBitVector(MAX_NUM_PROTOS); set_all_bits(AllProtosOn, WordsInVectorOfSize(MAX_NUM_PROTOS)); set_all_bits(AllConfigsOn, WordsInVectorOfSize(MAX_NUM_CONFIGS)); zero_all_bits(AllConfigsOff, WordsInVectorOfSize(MAX_NUM_CONFIGS)); for (int i = 0; i < MAX_NUM_CLASSES; i++) { BaselineCutoffs[i] = 0; } if (classify_use_pre_adapted_templates) { FILE *File; STRING Filename; Filename = imagefile; Filename += ADAPT_TEMPLATE_SUFFIX; File = fopen(Filename.string(), "rb"); if (File == NULL) { AdaptedTemplates = NewAdaptedTemplates(true); } else { cprintf("\nReading pre-adapted templates from %s ...\n", Filename.string()); fflush(stdout); AdaptedTemplates = ReadAdaptedTemplates(File); cprintf("\n"); fclose(File); PrintAdaptedTemplates(stdout, AdaptedTemplates); for (int i = 0; i < AdaptedTemplates->Templates->NumClasses; i++) { BaselineCutoffs[i] = CharNormCutoffs[i]; } } } else { if (AdaptedTemplates != NULL) free_adapted_templates(AdaptedTemplates); AdaptedTemplates = NewAdaptedTemplates(true); } } /* InitAdaptiveClassifier */ void Classify::ResetAdaptiveClassifierInternal() { if (classify_learning_debug_level > 0) { tprintf("Resetting adaptive classifier (NumAdaptationsFailed=%d)\n", NumAdaptationsFailed); } free_adapted_templates(AdaptedTemplates); AdaptedTemplates = NewAdaptedTemplates(true); if (BackupAdaptedTemplates != NULL) free_adapted_templates(BackupAdaptedTemplates); BackupAdaptedTemplates = NULL; NumAdaptationsFailed = 0; } // If there are backup adapted templates, switches to those, otherwise resets // the main adaptive classifier (because it is full.) void Classify::SwitchAdaptiveClassifier() { if (BackupAdaptedTemplates == NULL) { ResetAdaptiveClassifierInternal(); return; } if (classify_learning_debug_level > 0) { tprintf("Switch to backup adaptive classifier (NumAdaptationsFailed=%d)\n", NumAdaptationsFailed); } free_adapted_templates(AdaptedTemplates); AdaptedTemplates = BackupAdaptedTemplates; BackupAdaptedTemplates = NULL; NumAdaptationsFailed = 0; } // Resets the backup adaptive classifier to empty. void Classify::StartBackupAdaptiveClassifier() { if (BackupAdaptedTemplates != NULL) free_adapted_templates(BackupAdaptedTemplates); BackupAdaptedTemplates = NewAdaptedTemplates(true); } /*---------------------------------------------------------------------------*/ /** * This routine prepares the adaptive * matcher for the start * of the first pass. Learning is enabled (unless it * is disabled for the whole program). * * @note this is somewhat redundant, it simply says that if learning is * enabled then it will remain enabled on the first pass. If it is * disabled, then it will remain disabled. This is only put here to * make it very clear that learning is controlled directly by the global * setting of EnableLearning. * * Globals: * - #EnableLearning * set to TRUE by this routine * * @note Exceptions: none * @note History: Mon Apr 15 16:39:29 1991, DSJ, Created. */ void Classify::SettupPass1() { EnableLearning = classify_enable_learning; getDict().SettupStopperPass1(); } /* SettupPass1 */ /*---------------------------------------------------------------------------*/ /** * This routine prepares the adaptive * matcher for the start of the second pass. Further * learning is disabled. * * Globals: * - #EnableLearning set to FALSE by this routine * * @note Exceptions: none * @note History: Mon Apr 15 16:39:29 1991, DSJ, Created. */ void Classify::SettupPass2() { EnableLearning = FALSE; getDict().SettupStopperPass2(); } /* SettupPass2 */ /*---------------------------------------------------------------------------*/ /** * This routine creates a new adapted * class and uses Blob as the model for the first * config in that class. * * @param Blob blob to model new class after * @param ClassId id of the class to be initialized * @param FontinfoId font information inferred from pre-trained templates * @param Class adapted class to be initialized * @param Templates adapted templates to add new class to * * Globals: * - #AllProtosOn dummy mask with all 1's * - BaselineCutoffs kludge needed to get cutoffs * - #PreTrainedTemplates kludge needed to get cutoffs * * @note Exceptions: none * @note History: Thu Mar 14 12:49:39 1991, DSJ, Created. */ void Classify::InitAdaptedClass(TBLOB *Blob, CLASS_ID ClassId, int FontinfoId, ADAPT_CLASS Class, ADAPT_TEMPLATES Templates) { FEATURE_SET Features; int Fid, Pid; FEATURE Feature; int NumFeatures; TEMP_PROTO TempProto; PROTO Proto; INT_CLASS IClass; TEMP_CONFIG Config; classify_norm_method.set_value(baseline); Features = ExtractOutlineFeatures(Blob); NumFeatures = Features->NumFeatures; if (NumFeatures > UNLIKELY_NUM_FEAT || NumFeatures <= 0) { FreeFeatureSet(Features); return; } Config = NewTempConfig(NumFeatures - 1, FontinfoId); TempConfigFor(Class, 0) = Config; /* this is a kludge to construct cutoffs for adapted templates */ if (Templates == AdaptedTemplates) BaselineCutoffs[ClassId] = CharNormCutoffs[ClassId]; IClass = ClassForClassId (Templates->Templates, ClassId); for (Fid = 0; Fid < Features->NumFeatures; Fid++) { Pid = AddIntProto (IClass); assert (Pid != NO_PROTO); Feature = Features->Features[Fid]; TempProto = NewTempProto (); Proto = &(TempProto->Proto); /* compute proto params - NOTE that Y_DIM_OFFSET must be used because ConvertProto assumes that the Y dimension varies from -0.5 to 0.5 instead of the -0.25 to 0.75 used in baseline normalization */ Proto->Angle = Feature->Params[OutlineFeatDir]; Proto->X = Feature->Params[OutlineFeatX]; Proto->Y = Feature->Params[OutlineFeatY] - Y_DIM_OFFSET; Proto->Length = Feature->Params[OutlineFeatLength]; FillABC(Proto); TempProto->ProtoId = Pid; SET_BIT (Config->Protos, Pid); ConvertProto(Proto, Pid, IClass); AddProtoToProtoPruner(Proto, Pid, IClass, classify_learning_debug_level >= 2); Class->TempProtos = push (Class->TempProtos, TempProto); } FreeFeatureSet(Features); AddIntConfig(IClass); ConvertConfig (AllProtosOn, 0, IClass); if (classify_learning_debug_level >= 1) { tprintf("Added new class '%s' with class id %d and %d protos.\n", unicharset.id_to_unichar(ClassId), ClassId, NumFeatures); if (classify_learning_debug_level > 1) DisplayAdaptedChar(Blob, IClass); } if (IsEmptyAdaptedClass(Class)) (Templates->NumNonEmptyClasses)++; } /* InitAdaptedClass */ /*---------------------------------------------------------------------------*/ /** * This routine sets up the feature * extractor to extract baseline normalized * pico-features. * * The extracted pico-features are converted * to integer form and placed in IntFeatures. The * original floating-pt. features are returned in * FloatFeatures. * * Globals: none * @param Blob blob to extract features from * @param[out] IntFeatures array to fill with integer features * @param[out] FloatFeatures place to return actual floating-pt features * * @return Number of pico-features returned (0 if * an error occurred) * @note Exceptions: none * @note History: Tue Mar 12 17:55:18 1991, DSJ, Created. */ int Classify::GetAdaptiveFeatures(TBLOB *Blob, INT_FEATURE_ARRAY IntFeatures, FEATURE_SET *FloatFeatures) { FEATURE_SET Features; int NumFeatures; classify_norm_method.set_value(baseline); Features = ExtractPicoFeatures(Blob); NumFeatures = Features->NumFeatures; if (NumFeatures == 0 || NumFeatures > UNLIKELY_NUM_FEAT) { FreeFeatureSet(Features); return 0; } ComputeIntFeatures(Features, IntFeatures); *FloatFeatures = Features; return NumFeatures; } /* GetAdaptiveFeatures */ /*----------------------------------------------------------------------------- Private Code -----------------------------------------------------------------------------*/ /*---------------------------------------------------------------------------*/ /** * Return TRUE if the specified word is * acceptable for adaptation. * * Globals: none * * @param word current word * * @return TRUE or FALSE * @note Exceptions: none * @note History: Thu May 30 14:25:06 1991, DSJ, Created. */ bool Classify::AdaptableWord(WERD_RES* word) { if (word->best_choice == NULL) return false; int BestChoiceLength = word->best_choice->length(); float adaptable_score = getDict().segment_penalty_dict_case_ok + ADAPTABLE_WERD_ADJUSTMENT; return // rules that apply in general - simplest to compute first BestChoiceLength > 0 && BestChoiceLength == word->rebuild_word->NumBlobs() && BestChoiceLength <= MAX_ADAPTABLE_WERD_SIZE && // This basically ensures that the word is at least a dictionary match // (freq word, user word, system dawg word, etc). // Since all the other adjustments will make adjust factor higher // than higher than adaptable_score=1.1+0.05=1.15 // Since these are other flags that ensure that the word is dict word, // this check could be at times redundant. word->best_choice->adjust_factor() <= adaptable_score && // Make sure that alternative choices are not dictionary words. word->AlternativeChoiceAdjustmentsWorseThan(adaptable_score); } /*---------------------------------------------------------------------------*/ /** * @param Blob blob to add to templates for ClassId * @param ClassId class to add blob to * @param FontinfoId font information from pre-trained templates * @param Threshold minimum match rating to existing template * @param adaptive_templates current set of adapted templates * * Globals: * - AllProtosOn dummy mask to match against all protos * - AllConfigsOn dummy mask to match against all configs * * @return none * @note Exceptions: none * @note History: Thu Mar 14 09:36:03 1991, DSJ, Created. */ void Classify::AdaptToChar(TBLOB* Blob, CLASS_ID ClassId, int FontinfoId, FLOAT32 Threshold, ADAPT_TEMPLATES adaptive_templates) { int NumFeatures; INT_FEATURE_ARRAY IntFeatures; UnicharRating int_result; INT_CLASS IClass; ADAPT_CLASS Class; TEMP_CONFIG TempConfig; FEATURE_SET FloatFeatures; int NewTempConfigId; if (!LegalClassId (ClassId)) return; int_result.unichar_id = ClassId; Class = adaptive_templates->Class[ClassId]; assert(Class != NULL); if (IsEmptyAdaptedClass(Class)) { InitAdaptedClass(Blob, ClassId, FontinfoId, Class, adaptive_templates); } else { IClass = ClassForClassId(adaptive_templates->Templates, ClassId); NumFeatures = GetAdaptiveFeatures(Blob, IntFeatures, &FloatFeatures); if (NumFeatures <= 0) { return; // Features already freed by GetAdaptiveFeatures. } // Only match configs with the matching font. BIT_VECTOR MatchingFontConfigs = NewBitVector(MAX_NUM_PROTOS); for (int cfg = 0; cfg < IClass->NumConfigs; ++cfg) { if (GetFontinfoId(Class, cfg) == FontinfoId) { SET_BIT(MatchingFontConfigs, cfg); } else { reset_bit(MatchingFontConfigs, cfg); } } im_.Match(IClass, AllProtosOn, MatchingFontConfigs, NumFeatures, IntFeatures, &int_result, classify_adapt_feature_threshold, NO_DEBUG, matcher_debug_separate_windows); FreeBitVector(MatchingFontConfigs); SetAdaptiveThreshold(Threshold); if (1.0f - int_result.rating <= Threshold) { if (ConfigIsPermanent(Class, int_result.config)) { if (classify_learning_debug_level >= 1) tprintf("Found good match to perm config %d = %4.1f%%.\n", int_result.config, int_result.rating * 100.0); FreeFeatureSet(FloatFeatures); return; } TempConfig = TempConfigFor(Class, int_result.config); IncreaseConfidence(TempConfig); if (TempConfig->NumTimesSeen > Class->MaxNumTimesSeen) { Class->MaxNumTimesSeen = TempConfig->NumTimesSeen; } if (classify_learning_debug_level >= 1) tprintf("Increasing reliability of temp config %d to %d.\n", int_result.config, TempConfig->NumTimesSeen); if (TempConfigReliable(ClassId, TempConfig)) { MakePermanent(adaptive_templates, ClassId, int_result.config, Blob); UpdateAmbigsGroup(ClassId, Blob); } } else { if (classify_learning_debug_level >= 1) { tprintf("Found poor match to temp config %d = %4.1f%%.\n", int_result.config, int_result.rating * 100.0); if (classify_learning_debug_level > 2) DisplayAdaptedChar(Blob, IClass); } NewTempConfigId = MakeNewTemporaryConfig(adaptive_templates, ClassId, FontinfoId, NumFeatures, IntFeatures, FloatFeatures); if (NewTempConfigId >= 0 && TempConfigReliable(ClassId, TempConfigFor(Class, NewTempConfigId))) { MakePermanent(adaptive_templates, ClassId, NewTempConfigId, Blob); UpdateAmbigsGroup(ClassId, Blob); } #ifndef GRAPHICS_DISABLED if (classify_learning_debug_level > 1) { DisplayAdaptedChar(Blob, IClass); } #endif } FreeFeatureSet(FloatFeatures); } } /* AdaptToChar */ void Classify::DisplayAdaptedChar(TBLOB* blob, INT_CLASS_STRUCT* int_class) { #ifndef GRAPHICS_DISABLED INT_FX_RESULT_STRUCT fx_info; GenericVector bl_features; TrainingSample* sample = BlobToTrainingSample(*blob, classify_nonlinear_norm, &fx_info, &bl_features); if (sample == NULL) return; UnicharRating int_result; im_.Match(int_class, AllProtosOn, AllConfigsOn, bl_features.size(), &bl_features[0], &int_result, classify_adapt_feature_threshold, NO_DEBUG, matcher_debug_separate_windows); tprintf("Best match to temp config %d = %4.1f%%.\n", int_result.config, int_result.rating * 100.0); if (classify_learning_debug_level >= 2) { uinT32 ConfigMask; ConfigMask = 1 << int_result.config; ShowMatchDisplay(); im_.Match(int_class, AllProtosOn, (BIT_VECTOR)&ConfigMask, bl_features.size(), &bl_features[0], &int_result, classify_adapt_feature_threshold, 6 | 0x19, matcher_debug_separate_windows); UpdateMatchDisplay(); } delete sample; #endif } /** * This routine adds the result of a classification into * Results. If the new rating is much worse than the current * best rating, it is not entered into results because it * would end up being stripped later anyway. If the new rating * is better than the old rating for the class, it replaces the * old rating. If this is the first rating for the class, the * class is added to the list of matched classes in Results. * If the new rating is better than the best so far, it * becomes the best so far. * * Globals: * - #matcher_bad_match_pad defines limits of an acceptable match * * @param new_result new result to add * @param[out] results results to add new result to * * @note Exceptions: none * @note History: Tue Mar 12 18:19:29 1991, DSJ, Created. */ void Classify::AddNewResult(const UnicharRating& new_result, ADAPT_RESULTS *results) { int old_match = FindScoredUnichar(new_result.unichar_id, *results); if (new_result.rating + matcher_bad_match_pad < results->best_rating || (old_match < results->match.size() && new_result.rating <= results->match[old_match].rating)) return; // New one not good enough. if (!unicharset.get_fragment(new_result.unichar_id)) results->HasNonfragment = true; if (old_match < results->match.size()) { results->match[old_match].rating = new_result.rating; } else { results->match.push_back(new_result); } if (new_result.rating > results->best_rating && // Ensure that fragments do not affect best rating, class and config. // This is needed so that at least one non-fragmented character is // always present in the results. // TODO(daria): verify that this helps accuracy and does not // hurt performance. !unicharset.get_fragment(new_result.unichar_id)) { results->best_match_index = old_match; results->best_rating = new_result.rating; results->best_unichar_id = new_result.unichar_id; } } /* AddNewResult */ /*---------------------------------------------------------------------------*/ /** * This routine is identical to CharNormClassifier() * except that it does no class pruning. It simply matches * the unknown blob against the classes listed in * Ambiguities. * * Globals: * - #AllProtosOn mask that enables all protos * - #AllConfigsOn mask that enables all configs * * @param blob blob to be classified * @param templates built-in templates to classify against * @param classes adapted class templates * @param ambiguities array of unichar id's to match against * @param[out] results place to put match results * @param int_features * @param fx_info * * @note Exceptions: none * @note History: Tue Mar 12 19:40:36 1991, DSJ, Created. */ void Classify::AmbigClassifier( const GenericVector& int_features, const INT_FX_RESULT_STRUCT& fx_info, const TBLOB *blob, INT_TEMPLATES templates, ADAPT_CLASS *classes, UNICHAR_ID *ambiguities, ADAPT_RESULTS *results) { if (int_features.empty()) return; uinT8* CharNormArray = new uinT8[unicharset.size()]; UnicharRating int_result; results->BlobLength = GetCharNormFeature(fx_info, templates, NULL, CharNormArray); bool debug = matcher_debug_level >= 2 || classify_debug_level > 1; if (debug) tprintf("AM Matches = "); int top = blob->bounding_box().top(); int bottom = blob->bounding_box().bottom(); while (*ambiguities >= 0) { CLASS_ID class_id = *ambiguities; int_result.unichar_id = class_id; im_.Match(ClassForClassId(templates, class_id), AllProtosOn, AllConfigsOn, int_features.size(), &int_features[0], &int_result, classify_adapt_feature_threshold, NO_DEBUG, matcher_debug_separate_windows); ExpandShapesAndApplyCorrections(NULL, debug, class_id, bottom, top, 0, results->BlobLength, classify_integer_matcher_multiplier, CharNormArray, &int_result, results); ambiguities++; } delete [] CharNormArray; } /* AmbigClassifier */ /*---------------------------------------------------------------------------*/ /// Factored-out calls to IntegerMatcher based on class pruner results. /// Returns integer matcher results inside CLASS_PRUNER_RESULTS structure. void Classify::MasterMatcher(INT_TEMPLATES templates, inT16 num_features, const INT_FEATURE_STRUCT* features, const uinT8* norm_factors, ADAPT_CLASS* classes, int debug, int matcher_multiplier, const TBOX& blob_box, const GenericVector& results, ADAPT_RESULTS* final_results) { int top = blob_box.top(); int bottom = blob_box.bottom(); UnicharRating int_result; for (int c = 0; c < results.size(); c++) { CLASS_ID class_id = results[c].Class; BIT_VECTOR protos = classes != NULL ? classes[class_id]->PermProtos : AllProtosOn; BIT_VECTOR configs = classes != NULL ? classes[class_id]->PermConfigs : AllConfigsOn; int_result.unichar_id = class_id; im_.Match(ClassForClassId(templates, class_id), protos, configs, num_features, features, &int_result, classify_adapt_feature_threshold, debug, matcher_debug_separate_windows); bool debug = matcher_debug_level >= 2 || classify_debug_level > 1; ExpandShapesAndApplyCorrections(classes, debug, class_id, bottom, top, results[c].Rating, final_results->BlobLength, matcher_multiplier, norm_factors, &int_result, final_results); } } // Converts configs to fonts, and if the result is not adapted, and a // shape_table_ is present, the shape is expanded to include all // unichar_ids represented, before applying a set of corrections to the // distance rating in int_result, (see ComputeCorrectedRating.) // The results are added to the final_results output. void Classify::ExpandShapesAndApplyCorrections( ADAPT_CLASS* classes, bool debug, int class_id, int bottom, int top, float cp_rating, int blob_length, int matcher_multiplier, const uinT8* cn_factors, UnicharRating* int_result, ADAPT_RESULTS* final_results) { if (classes != NULL) { // Adapted result. Convert configs to fontinfo_ids. int_result->adapted = true; for (int f = 0; f < int_result->fonts.size(); ++f) { int_result->fonts[f].fontinfo_id = GetFontinfoId(classes[class_id], int_result->fonts[f].fontinfo_id); } } else { // Pre-trained result. Map fonts using font_sets_. int_result->adapted = false; for (int f = 0; f < int_result->fonts.size(); ++f) { int_result->fonts[f].fontinfo_id = ClassAndConfigIDToFontOrShapeID(class_id, int_result->fonts[f].fontinfo_id); } if (shape_table_ != NULL) { // Two possible cases: // 1. Flat shapetable. All unichar-ids of the shapes referenced by // int_result->fonts are the same. In this case build a new vector of // mapped fonts and replace the fonts in int_result. // 2. Multi-unichar shapetable. Variable unichars in the shapes referenced // by int_result. In this case, build a vector of UnicharRating to // gather together different font-ids for each unichar. Also covers case1. GenericVector mapped_results; for (int f = 0; f < int_result->fonts.size(); ++f) { int shape_id = int_result->fonts[f].fontinfo_id; const Shape& shape = shape_table_->GetShape(shape_id); for (int c = 0; c < shape.size(); ++c) { int unichar_id = shape[c].unichar_id; if (!unicharset.get_enabled(unichar_id)) continue; // Find the mapped_result for unichar_id. int r = 0; for (r = 0; r < mapped_results.size() && mapped_results[r].unichar_id != unichar_id; ++r) {} if (r == mapped_results.size()) { mapped_results.push_back(*int_result); mapped_results[r].unichar_id = unichar_id; mapped_results[r].fonts.truncate(0); } for (int i = 0; i < shape[c].font_ids.size(); ++i) { mapped_results[r].fonts.push_back( ScoredFont(shape[c].font_ids[i], int_result->fonts[f].score)); } } } for (int m = 0; m < mapped_results.size(); ++m) { mapped_results[m].rating = ComputeCorrectedRating(debug, mapped_results[m].unichar_id, cp_rating, int_result->rating, int_result->feature_misses, bottom, top, blob_length, matcher_multiplier, cn_factors); AddNewResult(mapped_results[m], final_results); } return; } } if (unicharset.get_enabled(class_id)) { int_result->rating = ComputeCorrectedRating(debug, class_id, cp_rating, int_result->rating, int_result->feature_misses, bottom, top, blob_length, matcher_multiplier, cn_factors); AddNewResult(*int_result, final_results); } } // Applies a set of corrections to the confidence im_rating, // including the cn_correction, miss penalty and additional penalty // for non-alnums being vertical misfits. Returns the corrected confidence. double Classify::ComputeCorrectedRating(bool debug, int unichar_id, double cp_rating, double im_rating, int feature_misses, int bottom, int top, int blob_length, int matcher_multiplier, const uinT8* cn_factors) { // Compute class feature corrections. double cn_corrected = im_.ApplyCNCorrection(1.0 - im_rating, blob_length, cn_factors[unichar_id], matcher_multiplier); double miss_penalty = tessedit_class_miss_scale * feature_misses; double vertical_penalty = 0.0; // Penalize non-alnums for being vertical misfits. if (!unicharset.get_isalpha(unichar_id) && !unicharset.get_isdigit(unichar_id) && cn_factors[unichar_id] != 0 && classify_misfit_junk_penalty > 0.0) { int min_bottom, max_bottom, min_top, max_top; unicharset.get_top_bottom(unichar_id, &min_bottom, &max_bottom, &min_top, &max_top); if (debug) { tprintf("top=%d, vs [%d, %d], bottom=%d, vs [%d, %d]\n", top, min_top, max_top, bottom, min_bottom, max_bottom); } if (top < min_top || top > max_top || bottom < min_bottom || bottom > max_bottom) { vertical_penalty = classify_misfit_junk_penalty; } } double result = 1.0 - (cn_corrected + miss_penalty + vertical_penalty); if (result < WORST_POSSIBLE_RATING) result = WORST_POSSIBLE_RATING; if (debug) { tprintf("%s: %2.1f%%(CP%2.1f, IM%2.1f + CN%.2f(%d) + MP%2.1f + VP%2.1f)\n", unicharset.id_to_unichar(unichar_id), result * 100.0, cp_rating * 100.0, (1.0 - im_rating) * 100.0, (cn_corrected - (1.0 - im_rating)) * 100.0, cn_factors[unichar_id], miss_penalty * 100.0, vertical_penalty * 100.0); } return result; } /*---------------------------------------------------------------------------*/ /** * This routine extracts baseline normalized features * from the unknown character and matches them against the * specified set of templates. The classes which match * are added to Results. * * Globals: * - BaselineCutoffs expected num features for each class * * @param Blob blob to be classified * @param Templates current set of adapted templates * @param Results place to put match results * @param int_features * @param fx_info * * @return Array of possible ambiguous chars that should be checked. * @note Exceptions: none * @note History: Tue Mar 12 19:38:03 1991, DSJ, Created. */ UNICHAR_ID *Classify::BaselineClassifier( TBLOB *Blob, const GenericVector& int_features, const INT_FX_RESULT_STRUCT& fx_info, ADAPT_TEMPLATES Templates, ADAPT_RESULTS *Results) { if (int_features.empty()) return NULL; uinT8* CharNormArray = new uinT8[unicharset.size()]; ClearCharNormArray(CharNormArray); Results->BlobLength = IntCastRounded(fx_info.Length / kStandardFeatureLength); PruneClasses(Templates->Templates, int_features.size(), -1, &int_features[0], CharNormArray, BaselineCutoffs, &Results->CPResults); if (matcher_debug_level >= 2 || classify_debug_level > 1) tprintf("BL Matches = "); MasterMatcher(Templates->Templates, int_features.size(), &int_features[0], CharNormArray, Templates->Class, matcher_debug_flags, 0, Blob->bounding_box(), Results->CPResults, Results); delete [] CharNormArray; CLASS_ID ClassId = Results->best_unichar_id; if (ClassId == INVALID_UNICHAR_ID || Results->best_match_index < 0) return NULL; return Templates->Class[ClassId]-> Config[Results->match[Results->best_match_index].config].Perm->Ambigs; } /* BaselineClassifier */ /*---------------------------------------------------------------------------*/ /** * This routine extracts character normalized features * from the unknown character and matches them against the * specified set of templates. The classes which match * are added to Results. * * @param blob blob to be classified * @param sample templates to classify unknown against * @param adapt_results place to put match results * * Globals: * - CharNormCutoffs expected num features for each class * - AllProtosOn mask that enables all protos * - AllConfigsOn mask that enables all configs * * @note Exceptions: none * @note History: Tue Mar 12 16:02:52 1991, DSJ, Created. */ int Classify::CharNormClassifier(TBLOB *blob, const TrainingSample& sample, ADAPT_RESULTS *adapt_results) { // This is the length that is used for scaling ratings vs certainty. adapt_results->BlobLength = IntCastRounded(sample.outline_length() / kStandardFeatureLength); GenericVector unichar_results; static_classifier_->UnicharClassifySample(sample, blob->denorm().pix(), 0, -1, &unichar_results); // Convert results to the format used internally by AdaptiveClassifier. for (int r = 0; r < unichar_results.size(); ++r) { AddNewResult(unichar_results[r], adapt_results); } return sample.num_features(); } /* CharNormClassifier */ // As CharNormClassifier, but operates on a TrainingSample and outputs to // a GenericVector of ShapeRating without conversion to classes. int Classify::CharNormTrainingSample(bool pruner_only, int keep_this, const TrainingSample& sample, GenericVector* results) { results->clear(); ADAPT_RESULTS* adapt_results = new ADAPT_RESULTS(); adapt_results->Initialize(); // Compute the bounding box of the features. int num_features = sample.num_features(); // Only the top and bottom of the blob_box are used by MasterMatcher, so // fabricate right and left using top and bottom. TBOX blob_box(sample.geo_feature(GeoBottom), sample.geo_feature(GeoBottom), sample.geo_feature(GeoTop), sample.geo_feature(GeoTop)); // Compute the char_norm_array from the saved cn_feature. FEATURE norm_feature = sample.GetCNFeature(); uinT8* char_norm_array = new uinT8[unicharset.size()]; int num_pruner_classes = MAX(unicharset.size(), PreTrainedTemplates->NumClasses); uinT8* pruner_norm_array = new uinT8[num_pruner_classes]; adapt_results->BlobLength = static_cast(ActualOutlineLength(norm_feature) * 20 + 0.5); ComputeCharNormArrays(norm_feature, PreTrainedTemplates, char_norm_array, pruner_norm_array); PruneClasses(PreTrainedTemplates, num_features, keep_this, sample.features(), pruner_norm_array, shape_table_ != NULL ? &shapetable_cutoffs_[0] : CharNormCutoffs, &adapt_results->CPResults); delete [] pruner_norm_array; if (keep_this >= 0) { adapt_results->CPResults[0].Class = keep_this; adapt_results->CPResults.truncate(1); } if (pruner_only) { // Convert pruner results to output format. for (int i = 0; i < adapt_results->CPResults.size(); ++i) { int class_id = adapt_results->CPResults[i].Class; results->push_back( UnicharRating(class_id, 1.0f - adapt_results->CPResults[i].Rating)); } } else { MasterMatcher(PreTrainedTemplates, num_features, sample.features(), char_norm_array, NULL, matcher_debug_flags, classify_integer_matcher_multiplier, blob_box, adapt_results->CPResults, adapt_results); // Convert master matcher results to output format. for (int i = 0; i < adapt_results->match.size(); i++) { results->push_back(adapt_results->match[i]); } results->sort(&UnicharRating::SortDescendingRating); } delete [] char_norm_array; delete adapt_results; return num_features; } /* CharNormTrainingSample */ /*---------------------------------------------------------------------------*/ /** * This routine computes a rating which reflects the * likelihood that the blob being classified is a noise * blob. NOTE: assumes that the blob length has already been * computed and placed into Results. * * @param results results to add noise classification to * * Globals: * - matcher_avg_noise_size avg. length of a noise blob * * @note Exceptions: none * @note History: Tue Mar 12 18:36:52 1991, DSJ, Created. */ void Classify::ClassifyAsNoise(ADAPT_RESULTS *results) { float rating = results->BlobLength / matcher_avg_noise_size; rating *= rating; rating /= 1.0 + rating; AddNewResult(UnicharRating(UNICHAR_SPACE, 1.0f - rating), results); } /* ClassifyAsNoise */ /// The function converts the given match ratings to the list of blob /// choices with ratings and certainties (used by the context checkers). /// If character fragments are present in the results, this function also makes /// sure that there is at least one non-fragmented classification included. /// For each classification result check the unicharset for "definite" /// ambiguities and modify the resulting Choices accordingly. void Classify::ConvertMatchesToChoices(const DENORM& denorm, const TBOX& box, ADAPT_RESULTS *Results, BLOB_CHOICE_LIST *Choices) { assert(Choices != NULL); FLOAT32 Rating; FLOAT32 Certainty; BLOB_CHOICE_IT temp_it; bool contains_nonfrag = false; temp_it.set_to_list(Choices); int choices_length = 0; // With no shape_table_ maintain the previous MAX_MATCHES as the maximum // number of returned results, but with a shape_table_ we want to have room // for at least the biggest shape (which might contain hundreds of Indic // grapheme fragments) and more, so use double the size of the biggest shape // if that is more than the default. int max_matches = MAX_MATCHES; if (shape_table_ != NULL) { max_matches = shape_table_->MaxNumUnichars() * 2; if (max_matches < MAX_MATCHES) max_matches = MAX_MATCHES; } float best_certainty = -MAX_FLOAT32; for (int i = 0; i < Results->match.size(); i++) { const UnicharRating& result = Results->match[i]; bool adapted = result.adapted; bool current_is_frag = (unicharset.get_fragment(result.unichar_id) != NULL); if (temp_it.length()+1 == max_matches && !contains_nonfrag && current_is_frag) { continue; // look for a non-fragmented character to fill the // last spot in Choices if only fragments are present } // BlobLength can never be legally 0, this means recognition failed. // But we must return a classification result because some invoking // functions (chopper/permuter) do not anticipate a null blob choice. // So we need to assign a poor, but not infinitely bad score. if (Results->BlobLength == 0) { Certainty = -20; Rating = 100; // should be -certainty * real_blob_length } else { Rating = Certainty = (1.0f - result.rating); Rating *= rating_scale * Results->BlobLength; Certainty *= -(getDict().certainty_scale); } // Adapted results, by their very nature, should have good certainty. // Those that don't are at best misleading, and often lead to errors, // so don't accept adapted results that are too far behind the best result, // whether adapted or static. // TODO(rays) find some way of automatically tuning these constants. if (Certainty > best_certainty) { best_certainty = MIN(Certainty, classify_adapted_pruning_threshold); } else if (adapted && Certainty / classify_adapted_pruning_factor < best_certainty) { continue; // Don't accept bad adapted results. } float min_xheight, max_xheight, yshift; denorm.XHeightRange(result.unichar_id, unicharset, box, &min_xheight, &max_xheight, &yshift); BLOB_CHOICE* choice = new BLOB_CHOICE(result.unichar_id, Rating, Certainty, unicharset.get_script(result.unichar_id), min_xheight, max_xheight, yshift, adapted ? BCC_ADAPTED_CLASSIFIER : BCC_STATIC_CLASSIFIER); choice->set_fonts(result.fonts); temp_it.add_to_end(choice); contains_nonfrag |= !current_is_frag; // update contains_nonfrag choices_length++; if (choices_length >= max_matches) break; } Results->match.truncate(choices_length); } // ConvertMatchesToChoices /*---------------------------------------------------------------------------*/ #ifndef GRAPHICS_DISABLED /** * * @param blob blob whose classification is being debugged * @param Results results of match being debugged * * Globals: none * * @note Exceptions: none * @note History: Wed Mar 13 16:44:41 1991, DSJ, Created. */ void Classify::DebugAdaptiveClassifier(TBLOB *blob, ADAPT_RESULTS *Results) { if (static_classifier_ == NULL) return; INT_FX_RESULT_STRUCT fx_info; GenericVector bl_features; TrainingSample* sample = BlobToTrainingSample(*blob, false, &fx_info, &bl_features); if (sample == NULL) return; static_classifier_->DebugDisplay(*sample, blob->denorm().pix(), Results->best_unichar_id); } /* DebugAdaptiveClassifier */ #endif /*---------------------------------------------------------------------------*/ /** * This routine performs an adaptive classification. * If we have not yet adapted to enough classes, a simple * classification to the pre-trained templates is performed. * Otherwise, we match the blob against the adapted templates. * If the adapted templates do not match well, we try a * match against the pre-trained templates. If an adapted * template match is found, we do a match to any pre-trained * templates which could be ambiguous. The results from all * of these classifications are merged together into Results. * * @param Blob blob to be classified * @param Results place to put match results * * Globals: * - PreTrainedTemplates built-in training templates * - AdaptedTemplates templates adapted for this page * - matcher_reliable_adaptive_result rating limit for a great match * * @note Exceptions: none * @note History: Tue Mar 12 08:50:11 1991, DSJ, Created. */ void Classify::DoAdaptiveMatch(TBLOB *Blob, ADAPT_RESULTS *Results) { UNICHAR_ID *Ambiguities; INT_FX_RESULT_STRUCT fx_info; GenericVector bl_features; TrainingSample* sample = BlobToTrainingSample(*Blob, classify_nonlinear_norm, &fx_info, &bl_features); if (sample == NULL) return; if (AdaptedTemplates->NumPermClasses < matcher_permanent_classes_min || tess_cn_matching) { CharNormClassifier(Blob, *sample, Results); } else { Ambiguities = BaselineClassifier(Blob, bl_features, fx_info, AdaptedTemplates, Results); if ((!Results->match.empty() && MarginalMatch(Results->best_rating, matcher_reliable_adaptive_result) && !tess_bn_matching) || Results->match.empty()) { CharNormClassifier(Blob, *sample, Results); } else if (Ambiguities && *Ambiguities >= 0 && !tess_bn_matching) { AmbigClassifier(bl_features, fx_info, Blob, PreTrainedTemplates, AdaptedTemplates->Class, Ambiguities, Results); } } // Force the blob to be classified as noise // if the results contain only fragments. // TODO(daria): verify that this is better than // just adding a NULL classification. if (!Results->HasNonfragment || Results->match.empty()) ClassifyAsNoise(Results); delete sample; } /* DoAdaptiveMatch */ /*---------------------------------------------------------------------------*/ /** * This routine matches blob to the built-in templates * to find out if there are any classes other than the correct * class which are potential ambiguities. * * @param Blob blob to get classification ambiguities for * @param CorrectClass correct class for Blob * * Globals: * - CurrentRatings used by qsort compare routine * - PreTrainedTemplates built-in templates * * @return String containing all possible ambiguous classes. * @note Exceptions: none * @note History: Fri Mar 15 08:08:22 1991, DSJ, Created. */ UNICHAR_ID *Classify::GetAmbiguities(TBLOB *Blob, CLASS_ID CorrectClass) { ADAPT_RESULTS *Results = new ADAPT_RESULTS(); UNICHAR_ID *Ambiguities; int i; Results->Initialize(); INT_FX_RESULT_STRUCT fx_info; GenericVector bl_features; TrainingSample* sample = BlobToTrainingSample(*Blob, classify_nonlinear_norm, &fx_info, &bl_features); if (sample == NULL) { delete Results; return NULL; } CharNormClassifier(Blob, *sample, Results); delete sample; RemoveBadMatches(Results); Results->match.sort(&UnicharRating::SortDescendingRating); /* copy the class id's into an string of ambiguities - don't copy if the correct class is the only class id matched */ Ambiguities = new UNICHAR_ID[Results->match.size() + 1]; if (Results->match.size() > 1 || (Results->match.size() == 1 && Results->match[0].unichar_id != CorrectClass)) { for (i = 0; i < Results->match.size(); i++) Ambiguities[i] = Results->match[i].unichar_id; Ambiguities[i] = -1; } else { Ambiguities[0] = -1; } delete Results; return Ambiguities; } /* GetAmbiguities */ // Returns true if the given blob looks too dissimilar to any character // present in the classifier templates. bool Classify::LooksLikeGarbage(TBLOB *blob) { BLOB_CHOICE_LIST *ratings = new BLOB_CHOICE_LIST(); AdaptiveClassifier(blob, ratings); BLOB_CHOICE_IT ratings_it(ratings); const UNICHARSET &unicharset = getDict().getUnicharset(); if (classify_debug_character_fragments) { print_ratings_list("======================\nLooksLikeGarbage() got ", ratings, unicharset); } for (ratings_it.mark_cycle_pt(); !ratings_it.cycled_list(); ratings_it.forward()) { if (unicharset.get_fragment(ratings_it.data()->unichar_id()) != NULL) { continue; } float certainty = ratings_it.data()->certainty(); delete ratings; return certainty < classify_character_fragments_garbage_certainty_threshold; } delete ratings; return true; // no whole characters in ratings } /*---------------------------------------------------------------------------*/ /** * This routine calls the integer (Hardware) feature * extractor if it has not been called before for this blob. * * The results from the feature extractor are placed into * globals so that they can be used in other routines without * re-extracting the features. * * It then copies the char norm features into the IntFeatures * array provided by the caller. * * @param templates used to compute char norm adjustments * @param pruner_norm_array Array of factors from blob normalization * process * @param char_norm_array array to fill with dummy char norm adjustments * @param fx_info * * Globals: * * @return Number of features extracted or 0 if an error occurred. * @note Exceptions: none * @note History: Tue May 28 10:40:52 1991, DSJ, Created. */ int Classify::GetCharNormFeature(const INT_FX_RESULT_STRUCT& fx_info, INT_TEMPLATES templates, uinT8* pruner_norm_array, uinT8* char_norm_array) { FEATURE norm_feature = NewFeature(&CharNormDesc); float baseline = kBlnBaselineOffset; float scale = MF_SCALE_FACTOR; norm_feature->Params[CharNormY] = (fx_info.Ymean - baseline) * scale; norm_feature->Params[CharNormLength] = fx_info.Length * scale / LENGTH_COMPRESSION; norm_feature->Params[CharNormRx] = fx_info.Rx * scale; norm_feature->Params[CharNormRy] = fx_info.Ry * scale; // Deletes norm_feature. ComputeCharNormArrays(norm_feature, templates, char_norm_array, pruner_norm_array); return IntCastRounded(fx_info.Length / kStandardFeatureLength); } /* GetCharNormFeature */ // Computes the char_norm_array for the unicharset and, if not NULL, the // pruner_array as appropriate according to the existence of the shape_table. void Classify::ComputeCharNormArrays(FEATURE_STRUCT* norm_feature, INT_TEMPLATES_STRUCT* templates, uinT8* char_norm_array, uinT8* pruner_array) { ComputeIntCharNormArray(*norm_feature, char_norm_array); if (pruner_array != NULL) { if (shape_table_ == NULL) { ComputeIntCharNormArray(*norm_feature, pruner_array); } else { memset(pruner_array, MAX_UINT8, templates->NumClasses * sizeof(pruner_array[0])); // Each entry in the pruner norm array is the MIN of all the entries of // the corresponding unichars in the CharNormArray. for (int id = 0; id < templates->NumClasses; ++id) { int font_set_id = templates->Class[id]->font_set_id; const FontSet &fs = fontset_table_.get(font_set_id); for (int config = 0; config < fs.size; ++config) { const Shape& shape = shape_table_->GetShape(fs.configs[config]); for (int c = 0; c < shape.size(); ++c) { if (char_norm_array[shape[c].unichar_id] < pruner_array[id]) pruner_array[id] = char_norm_array[shape[c].unichar_id]; } } } } } FreeFeature(norm_feature); } /*---------------------------------------------------------------------------*/ /** * * @param Templates adapted templates to add new config to * @param ClassId class id to associate with new config * @param FontinfoId font information inferred from pre-trained templates * @param NumFeatures number of features in IntFeatures * @param Features features describing model for new config * @param FloatFeatures floating-pt representation of features * * @return The id of the new config created, a negative integer in * case of error. * @note Exceptions: none * @note History: Fri Mar 15 08:49:46 1991, DSJ, Created. */ int Classify::MakeNewTemporaryConfig(ADAPT_TEMPLATES Templates, CLASS_ID ClassId, int FontinfoId, int NumFeatures, INT_FEATURE_ARRAY Features, FEATURE_SET FloatFeatures) { INT_CLASS IClass; ADAPT_CLASS Class; PROTO_ID OldProtos[MAX_NUM_PROTOS]; FEATURE_ID BadFeatures[MAX_NUM_INT_FEATURES]; int NumOldProtos; int NumBadFeatures; int MaxProtoId, OldMaxProtoId; int BlobLength = 0; int MaskSize; int ConfigId; TEMP_CONFIG Config; int i; int debug_level = NO_DEBUG; if (classify_learning_debug_level >= 3) debug_level = PRINT_MATCH_SUMMARY | PRINT_FEATURE_MATCHES | PRINT_PROTO_MATCHES; IClass = ClassForClassId(Templates->Templates, ClassId); Class = Templates->Class[ClassId]; if (IClass->NumConfigs >= MAX_NUM_CONFIGS) { ++NumAdaptationsFailed; if (classify_learning_debug_level >= 1) cprintf("Cannot make new temporary config: maximum number exceeded.\n"); return -1; } OldMaxProtoId = IClass->NumProtos - 1; NumOldProtos = im_.FindGoodProtos(IClass, AllProtosOn, AllConfigsOff, BlobLength, NumFeatures, Features, OldProtos, classify_adapt_proto_threshold, debug_level); MaskSize = WordsInVectorOfSize(MAX_NUM_PROTOS); zero_all_bits(TempProtoMask, MaskSize); for (i = 0; i < NumOldProtos; i++) SET_BIT(TempProtoMask, OldProtos[i]); NumBadFeatures = im_.FindBadFeatures(IClass, TempProtoMask, AllConfigsOn, BlobLength, NumFeatures, Features, BadFeatures, classify_adapt_feature_threshold, debug_level); MaxProtoId = MakeNewTempProtos(FloatFeatures, NumBadFeatures, BadFeatures, IClass, Class, TempProtoMask); if (MaxProtoId == NO_PROTO) { ++NumAdaptationsFailed; if (classify_learning_debug_level >= 1) cprintf("Cannot make new temp protos: maximum number exceeded.\n"); return -1; } ConfigId = AddIntConfig(IClass); ConvertConfig(TempProtoMask, ConfigId, IClass); Config = NewTempConfig(MaxProtoId, FontinfoId); TempConfigFor(Class, ConfigId) = Config; copy_all_bits(TempProtoMask, Config->Protos, Config->ProtoVectorSize); if (classify_learning_debug_level >= 1) cprintf("Making new temp config %d fontinfo id %d" " using %d old and %d new protos.\n", ConfigId, Config->FontinfoId, NumOldProtos, MaxProtoId - OldMaxProtoId); return ConfigId; } /* MakeNewTemporaryConfig */ /*---------------------------------------------------------------------------*/ /** * This routine finds sets of sequential bad features * that all have the same angle and converts each set into * a new temporary proto. The temp proto is added to the * proto pruner for IClass, pushed onto the list of temp * protos in Class, and added to TempProtoMask. * * @param Features floating-pt features describing new character * @param NumBadFeat number of bad features to turn into protos * @param BadFeat feature id's of bad features * @param IClass integer class templates to add new protos to * @param Class adapted class templates to add new protos to * @param TempProtoMask proto mask to add new protos to * * Globals: none * * @return Max proto id in class after all protos have been added. * Exceptions: none * History: Fri Mar 15 11:39:38 1991, DSJ, Created. */ PROTO_ID Classify::MakeNewTempProtos(FEATURE_SET Features, int NumBadFeat, FEATURE_ID BadFeat[], INT_CLASS IClass, ADAPT_CLASS Class, BIT_VECTOR TempProtoMask) { FEATURE_ID *ProtoStart; FEATURE_ID *ProtoEnd; FEATURE_ID *LastBad; TEMP_PROTO TempProto; PROTO Proto; FEATURE F1, F2; FLOAT32 X1, X2, Y1, Y2; FLOAT32 A1, A2, AngleDelta; FLOAT32 SegmentLength; PROTO_ID Pid; for (ProtoStart = BadFeat, LastBad = ProtoStart + NumBadFeat; ProtoStart < LastBad; ProtoStart = ProtoEnd) { F1 = Features->Features[*ProtoStart]; X1 = F1->Params[PicoFeatX]; Y1 = F1->Params[PicoFeatY]; A1 = F1->Params[PicoFeatDir]; for (ProtoEnd = ProtoStart + 1, SegmentLength = GetPicoFeatureLength(); ProtoEnd < LastBad; ProtoEnd++, SegmentLength += GetPicoFeatureLength()) { F2 = Features->Features[*ProtoEnd]; X2 = F2->Params[PicoFeatX]; Y2 = F2->Params[PicoFeatY]; A2 = F2->Params[PicoFeatDir]; AngleDelta = fabs(A1 - A2); if (AngleDelta > 0.5) AngleDelta = 1.0 - AngleDelta; if (AngleDelta > matcher_clustering_max_angle_delta || fabs(X1 - X2) > SegmentLength || fabs(Y1 - Y2) > SegmentLength) break; } F2 = Features->Features[*(ProtoEnd - 1)]; X2 = F2->Params[PicoFeatX]; Y2 = F2->Params[PicoFeatY]; A2 = F2->Params[PicoFeatDir]; Pid = AddIntProto(IClass); if (Pid == NO_PROTO) return (NO_PROTO); TempProto = NewTempProto(); Proto = &(TempProto->Proto); /* compute proto params - NOTE that Y_DIM_OFFSET must be used because ConvertProto assumes that the Y dimension varies from -0.5 to 0.5 instead of the -0.25 to 0.75 used in baseline normalization */ Proto->Length = SegmentLength; Proto->Angle = A1; Proto->X = (X1 + X2) / 2.0; Proto->Y = (Y1 + Y2) / 2.0 - Y_DIM_OFFSET; FillABC(Proto); TempProto->ProtoId = Pid; SET_BIT(TempProtoMask, Pid); ConvertProto(Proto, Pid, IClass); AddProtoToProtoPruner(Proto, Pid, IClass, classify_learning_debug_level >= 2); Class->TempProtos = push(Class->TempProtos, TempProto); } return IClass->NumProtos - 1; } /* MakeNewTempProtos */ /*---------------------------------------------------------------------------*/ /** * * @param Templates current set of adaptive templates * @param ClassId class containing config to be made permanent * @param ConfigId config to be made permanent * @param Blob current blob being adapted to * * Globals: none * * @note Exceptions: none * @note History: Thu Mar 14 15:54:08 1991, DSJ, Created. */ void Classify::MakePermanent(ADAPT_TEMPLATES Templates, CLASS_ID ClassId, int ConfigId, TBLOB *Blob) { UNICHAR_ID *Ambigs; TEMP_CONFIG Config; ADAPT_CLASS Class; PROTO_KEY ProtoKey; Class = Templates->Class[ClassId]; Config = TempConfigFor(Class, ConfigId); MakeConfigPermanent(Class, ConfigId); if (Class->NumPermConfigs == 0) Templates->NumPermClasses++; Class->NumPermConfigs++; // Initialize permanent config. Ambigs = GetAmbiguities(Blob, ClassId); PERM_CONFIG Perm = (PERM_CONFIG) alloc_struct(sizeof(PERM_CONFIG_STRUCT), "PERM_CONFIG_STRUCT"); Perm->Ambigs = Ambigs; Perm->FontinfoId = Config->FontinfoId; // Free memory associated with temporary config (since ADAPTED_CONFIG // is a union we need to clean up before we record permanent config). ProtoKey.Templates = Templates; ProtoKey.ClassId = ClassId; ProtoKey.ConfigId = ConfigId; Class->TempProtos = delete_d(Class->TempProtos, &ProtoKey, MakeTempProtoPerm); FreeTempConfig(Config); // Record permanent config. PermConfigFor(Class, ConfigId) = Perm; if (classify_learning_debug_level >= 1) { tprintf("Making config %d for %s (ClassId %d) permanent:" " fontinfo id %d, ambiguities '", ConfigId, getDict().getUnicharset().debug_str(ClassId).string(), ClassId, PermConfigFor(Class, ConfigId)->FontinfoId); for (UNICHAR_ID *AmbigsPointer = Ambigs; *AmbigsPointer >= 0; ++AmbigsPointer) tprintf("%s", unicharset.id_to_unichar(*AmbigsPointer)); tprintf("'.\n"); } } /* MakePermanent */ } // namespace tesseract /*---------------------------------------------------------------------------*/ /** * This routine converts TempProto to be permanent if * its proto id is used by the configuration specified in * ProtoKey. * * @param item1 (TEMP_PROTO) temporary proto to compare to key * @param item2 (PROTO_KEY) defines which protos to make permanent * * Globals: none * * @return TRUE if TempProto is converted, FALSE otherwise * @note Exceptions: none * @note History: Thu Mar 14 18:49:54 1991, DSJ, Created. */ int MakeTempProtoPerm(void *item1, void *item2) { ADAPT_CLASS Class; TEMP_CONFIG Config; TEMP_PROTO TempProto; PROTO_KEY *ProtoKey; TempProto = (TEMP_PROTO) item1; ProtoKey = (PROTO_KEY *) item2; Class = ProtoKey->Templates->Class[ProtoKey->ClassId]; Config = TempConfigFor(Class, ProtoKey->ConfigId); if (TempProto->ProtoId > Config->MaxProtoId || !test_bit (Config->Protos, TempProto->ProtoId)) return FALSE; MakeProtoPermanent(Class, TempProto->ProtoId); AddProtoToClassPruner(&(TempProto->Proto), ProtoKey->ClassId, ProtoKey->Templates->Templates); FreeTempProto(TempProto); return TRUE; } /* MakeTempProtoPerm */ /*---------------------------------------------------------------------------*/ namespace tesseract { /** * This routine writes the matches in Results to File. * * @param results match results to write to File * * Globals: none * * @note Exceptions: none * @note History: Mon Mar 18 09:24:53 1991, DSJ, Created. */ void Classify::PrintAdaptiveMatchResults(const ADAPT_RESULTS& results) { for (int i = 0; i < results.match.size(); ++i) { tprintf("%s ", unicharset.debug_str(results.match[i].unichar_id).string()); results.match[i].Print(); } } /* PrintAdaptiveMatchResults */ /*---------------------------------------------------------------------------*/ /** * This routine steps through each matching class in Results * and removes it from the match list if its rating * is worse than the BestRating plus a pad. In other words, * all good matches get moved to the front of the classes * array. * * @param Results contains matches to be filtered * * Globals: * - matcher_bad_match_pad defines a "bad match" * * @note Exceptions: none * @note History: Tue Mar 12 13:51:03 1991, DSJ, Created. */ void Classify::RemoveBadMatches(ADAPT_RESULTS *Results) { int Next, NextGood; FLOAT32 BadMatchThreshold; static const char* romans = "i v x I V X"; BadMatchThreshold = Results->best_rating - matcher_bad_match_pad; if (classify_bln_numeric_mode) { UNICHAR_ID unichar_id_one = unicharset.contains_unichar("1") ? unicharset.unichar_to_id("1") : -1; UNICHAR_ID unichar_id_zero = unicharset.contains_unichar("0") ? unicharset.unichar_to_id("0") : -1; float scored_one = ScoredUnichar(unichar_id_one, *Results); float scored_zero = ScoredUnichar(unichar_id_zero, *Results); for (Next = NextGood = 0; Next < Results->match.size(); Next++) { const UnicharRating& match = Results->match[Next]; if (match.rating >= BadMatchThreshold) { if (!unicharset.get_isalpha(match.unichar_id) || strstr(romans, unicharset.id_to_unichar(match.unichar_id)) != NULL) { } else if (unicharset.eq(match.unichar_id, "l") && scored_one < BadMatchThreshold) { Results->match[Next].unichar_id = unichar_id_one; } else if (unicharset.eq(match.unichar_id, "O") && scored_zero < BadMatchThreshold) { Results->match[Next].unichar_id = unichar_id_zero; } else { Results->match[Next].unichar_id = INVALID_UNICHAR_ID; // Don't copy. } if (Results->match[Next].unichar_id != INVALID_UNICHAR_ID) { if (NextGood == Next) { ++NextGood; } else { Results->match[NextGood++] = Results->match[Next]; } } } } } else { for (Next = NextGood = 0; Next < Results->match.size(); Next++) { if (Results->match[Next].rating >= BadMatchThreshold) { if (NextGood == Next) { ++NextGood; } else { Results->match[NextGood++] = Results->match[Next]; } } } } Results->match.truncate(NextGood); } /* RemoveBadMatches */ /*----------------------------------------------------------------------------*/ /** * This routine discards extra digits or punctuation from the results. * We keep only the top 2 punctuation answers and the top 1 digit answer if * present. * * @param Results contains matches to be filtered * * @note History: Tue Mar 12 13:51:03 1991, DSJ, Created. */ void Classify::RemoveExtraPuncs(ADAPT_RESULTS *Results) { int Next, NextGood; int punc_count; /*no of garbage characters */ int digit_count; /*garbage characters */ static char punc_chars[] = ". , ; : / ` ~ ' - = \\ | \" ! _ ^"; static char digit_chars[] = "0 1 2 3 4 5 6 7 8 9"; punc_count = 0; digit_count = 0; for (Next = NextGood = 0; Next < Results->match.size(); Next++) { const UnicharRating& match = Results->match[Next]; bool keep = true; if (strstr(punc_chars, unicharset.id_to_unichar(match.unichar_id)) != NULL) { if (punc_count >= 2) keep = false; punc_count++; } else { if (strstr(digit_chars, unicharset.id_to_unichar(match.unichar_id)) != NULL) { if (digit_count >= 1) keep = false; digit_count++; } } if (keep) { if (NextGood == Next) { ++NextGood; } else { Results->match[NextGood++] = match; } } } Results->match.truncate(NextGood); } /* RemoveExtraPuncs */ /*---------------------------------------------------------------------------*/ /** * This routine resets the internal thresholds inside * the integer matcher to correspond to the specified * threshold. * * @param Threshold threshold for creating new templates * * Globals: * - matcher_good_threshold default good match rating * * @note Exceptions: none * @note History: Tue Apr 9 08:33:13 1991, DSJ, Created. */ void Classify::SetAdaptiveThreshold(FLOAT32 Threshold) { Threshold = (Threshold == matcher_good_threshold) ? 0.9: (1.0 - Threshold); classify_adapt_proto_threshold.set_value( ClipToRange(255 * Threshold, 0, 255)); classify_adapt_feature_threshold.set_value( ClipToRange(255 * Threshold, 0, 255)); } /* SetAdaptiveThreshold */ /*---------------------------------------------------------------------------*/ /** * This routine displays debug information for the best config * of the given shape_id for the given set of features. * * @param shape_id classifier id to work with * @param features features of the unknown character * @param num_features Number of features in the features array. * * @note Exceptions: none * @note History: Fri Mar 22 08:43:52 1991, DSJ, Created. */ void Classify::ShowBestMatchFor(int shape_id, const INT_FEATURE_STRUCT* features, int num_features) { #ifndef GRAPHICS_DISABLED uinT32 config_mask; if (UnusedClassIdIn(PreTrainedTemplates, shape_id)) { tprintf("No built-in templates for class/shape %d\n", shape_id); return; } if (num_features <= 0) { tprintf("Illegal blob (char norm features)!\n"); return; } UnicharRating cn_result; classify_norm_method.set_value(character); im_.Match(ClassForClassId(PreTrainedTemplates, shape_id), AllProtosOn, AllConfigsOn, num_features, features, &cn_result, classify_adapt_feature_threshold, NO_DEBUG, matcher_debug_separate_windows); tprintf("\n"); config_mask = 1 << cn_result.config; tprintf("Static Shape ID: %d\n", shape_id); ShowMatchDisplay(); im_.Match(ClassForClassId(PreTrainedTemplates, shape_id), AllProtosOn, reinterpret_cast(&config_mask), num_features, features, &cn_result, classify_adapt_feature_threshold, matcher_debug_flags, matcher_debug_separate_windows); UpdateMatchDisplay(); #endif // GRAPHICS_DISABLED } /* ShowBestMatchFor */ // Returns a string for the classifier class_id: either the corresponding // unicharset debug_str or the shape_table_ debug str. STRING Classify::ClassIDToDebugStr(const INT_TEMPLATES_STRUCT* templates, int class_id, int config_id) const { STRING class_string; if (templates == PreTrainedTemplates && shape_table_ != NULL) { int shape_id = ClassAndConfigIDToFontOrShapeID(class_id, config_id); class_string = shape_table_->DebugStr(shape_id); } else { class_string = unicharset.debug_str(class_id); } return class_string; } // Converts a classifier class_id index to a shape_table_ index int Classify::ClassAndConfigIDToFontOrShapeID(int class_id, int int_result_config) const { int font_set_id = PreTrainedTemplates->Class[class_id]->font_set_id; // Older inttemps have no font_ids. if (font_set_id < 0) return kBlankFontinfoId; const FontSet &fs = fontset_table_.get(font_set_id); ASSERT_HOST(int_result_config >= 0 && int_result_config < fs.size); return fs.configs[int_result_config]; } // Converts a shape_table_ index to a classifier class_id index (not a // unichar-id!). Uses a search, so not fast. int Classify::ShapeIDToClassID(int shape_id) const { for (int id = 0; id < PreTrainedTemplates->NumClasses; ++id) { int font_set_id = PreTrainedTemplates->Class[id]->font_set_id; ASSERT_HOST(font_set_id >= 0); const FontSet &fs = fontset_table_.get(font_set_id); for (int config = 0; config < fs.size; ++config) { if (fs.configs[config] == shape_id) return id; } } tprintf("Shape %d not found\n", shape_id); return -1; } // Returns true if the given TEMP_CONFIG is good enough to make it // a permanent config. bool Classify::TempConfigReliable(CLASS_ID class_id, const TEMP_CONFIG &config) { if (classify_learning_debug_level >= 1) { tprintf("NumTimesSeen for config of %s is %d\n", getDict().getUnicharset().debug_str(class_id).string(), config->NumTimesSeen); } if (config->NumTimesSeen >= matcher_sufficient_examples_for_prototyping) { return true; } else if (config->NumTimesSeen < matcher_min_examples_for_prototyping) { return false; } else if (use_ambigs_for_adaption) { // Go through the ambigs vector and see whether we have already seen // enough times all the characters represented by the ambigs vector. const UnicharIdVector *ambigs = getDict().getUnicharAmbigs().AmbigsForAdaption(class_id); int ambigs_size = (ambigs == NULL) ? 0 : ambigs->size(); for (int ambig = 0; ambig < ambigs_size; ++ambig) { ADAPT_CLASS ambig_class = AdaptedTemplates->Class[(*ambigs)[ambig]]; assert(ambig_class != NULL); if (ambig_class->NumPermConfigs == 0 && ambig_class->MaxNumTimesSeen < matcher_min_examples_for_prototyping) { if (classify_learning_debug_level >= 1) { tprintf("Ambig %s has not been seen enough times," " not making config for %s permanent\n", getDict().getUnicharset().debug_str( (*ambigs)[ambig]).string(), getDict().getUnicharset().debug_str(class_id).string()); } return false; } } } return true; } void Classify::UpdateAmbigsGroup(CLASS_ID class_id, TBLOB *Blob) { const UnicharIdVector *ambigs = getDict().getUnicharAmbigs().ReverseAmbigsForAdaption(class_id); int ambigs_size = (ambigs == NULL) ? 0 : ambigs->size(); if (classify_learning_debug_level >= 1) { tprintf("Running UpdateAmbigsGroup for %s class_id=%d\n", getDict().getUnicharset().debug_str(class_id).string(), class_id); } for (int ambig = 0; ambig < ambigs_size; ++ambig) { CLASS_ID ambig_class_id = (*ambigs)[ambig]; const ADAPT_CLASS ambigs_class = AdaptedTemplates->Class[ambig_class_id]; for (int cfg = 0; cfg < MAX_NUM_CONFIGS; ++cfg) { if (ConfigIsPermanent(ambigs_class, cfg)) continue; const TEMP_CONFIG config = TempConfigFor(AdaptedTemplates->Class[ambig_class_id], cfg); if (config != NULL && TempConfigReliable(ambig_class_id, config)) { if (classify_learning_debug_level >= 1) { tprintf("Making config %d of %s permanent\n", cfg, getDict().getUnicharset().debug_str( ambig_class_id).string()); } MakePermanent(AdaptedTemplates, ambig_class_id, cfg, Blob); } } } } } // namespace tesseract