/////////////////////////////////////////////////////////////////////// // File: classify.cpp // Description: classify class. // Author: Samuel Charron // // (C) Copyright 2006, Google Inc. // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // http://www.apache.org/licenses/LICENSE-2.0 // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // /////////////////////////////////////////////////////////////////////// // Include automatically generated configuration file if running autoconf. #ifdef HAVE_CONFIG_H #include "config_auto.h" #endif #include "classify.h" #include "fontinfo.h" #include "intproto.h" #include "mfoutline.h" #include "scrollview.h" #include "shapeclassifier.h" #include "shapetable.h" #include "unicity_table.h" #include namespace tesseract { Classify::Classify() : BOOL_MEMBER(prioritize_division, FALSE, "Prioritize blob division over chopping", this->params()), INT_MEMBER(tessedit_single_match, FALSE, "Top choice only from CP", this->params()), BOOL_MEMBER(classify_enable_learning, true, "Enable adaptive classifier", this->params()), INT_MEMBER(classify_debug_level, 0, "Classify debug level", this->params()), INT_MEMBER(classify_norm_method, character, "Normalization Method ...", this->params()), double_MEMBER(classify_char_norm_range, 0.2, "Character Normalization Range ...", this->params()), double_MEMBER(classify_min_norm_scale_x, 0.0, "Min char x-norm scale ...", this->params()), /* PREV DEFAULT 0.1 */ double_MEMBER(classify_max_norm_scale_x, 0.325, "Max char x-norm scale ...", this->params()), /* PREV DEFAULT 0.3 */ double_MEMBER(classify_min_norm_scale_y, 0.0, "Min char y-norm scale ...", this->params()), /* PREV DEFAULT 0.1 */ double_MEMBER(classify_max_norm_scale_y, 0.325, "Max char y-norm scale ...", this->params()), /* PREV DEFAULT 0.3 */ double_MEMBER(classify_max_rating_ratio, 1.5, "Veto ratio between classifier ratings", this->params()), double_MEMBER(classify_max_certainty_margin, 5.5, "Veto difference between classifier certainties", this->params()), BOOL_MEMBER(tess_cn_matching, 0, "Character Normalized Matching", this->params()), BOOL_MEMBER(tess_bn_matching, 0, "Baseline Normalized Matching", this->params()), BOOL_MEMBER(classify_enable_adaptive_matcher, 1, "Enable adaptive classifier", this->params()), BOOL_MEMBER(classify_use_pre_adapted_templates, 0, "Use pre-adapted classifier templates", this->params()), BOOL_MEMBER(classify_save_adapted_templates, 0, "Save adapted templates to a file", this->params()), BOOL_MEMBER(classify_enable_adaptive_debugger, 0, "Enable match debugger", this->params()), BOOL_MEMBER(classify_nonlinear_norm, 0, "Non-linear stroke-density normalization", this->params()), INT_MEMBER(matcher_debug_level, 0, "Matcher Debug Level", this->params()), INT_MEMBER(matcher_debug_flags, 0, "Matcher Debug Flags", this->params()), INT_MEMBER(classify_learning_debug_level, 0, "Learning Debug Level: ", this->params()), double_MEMBER(matcher_good_threshold, 0.125, "Good Match (0-1)", this->params()), double_MEMBER(matcher_great_threshold, 0.0, "Great Match (0-1)", this->params()), double_MEMBER(matcher_perfect_threshold, 0.02, "Perfect Match (0-1)", this->params()), double_MEMBER(matcher_bad_match_pad, 0.15, "Bad Match Pad (0-1)", this->params()), double_MEMBER(matcher_rating_margin, 0.1, "New template margin (0-1)", this->params()), double_MEMBER(matcher_avg_noise_size, 12.0, "Avg. noise blob length", this->params()), INT_MEMBER(matcher_permanent_classes_min, 1, "Min # of permanent classes", this->params()), INT_MEMBER(matcher_min_examples_for_prototyping, 3, "Reliable Config Threshold", this->params()), INT_MEMBER(matcher_sufficient_examples_for_prototyping, 5, "Enable adaption even if the ambiguities have not been seen", this->params()), double_MEMBER(matcher_clustering_max_angle_delta, 0.015, "Maximum angle delta for prototype clustering", this->params()), double_MEMBER(classify_misfit_junk_penalty, 0.0, "Penalty to apply when a non-alnum is vertically out of " "its expected textline position", this->params()), double_MEMBER(rating_scale, 1.5, "Rating scaling factor", this->params()), double_MEMBER(certainty_scale, 20.0, "Certainty scaling factor", this->params()), double_MEMBER(tessedit_class_miss_scale, 0.00390625, "Scale factor for features not used", this->params()), double_MEMBER(classify_adapted_pruning_factor, 2.5, "Prune poor adapted results this much worse than best result", this->params()), double_MEMBER(classify_adapted_pruning_threshold, -1.0, "Threshold at which classify_adapted_pruning_factor starts", this->params()), INT_MEMBER(classify_adapt_proto_threshold, 230, "Threshold for good protos during adaptive 0-255", this->params()), INT_MEMBER(classify_adapt_feature_threshold, 230, "Threshold for good features during adaptive 0-255", this->params()), BOOL_MEMBER(disable_character_fragments, TRUE, "Do not include character fragments in the" " results of the classifier", this->params()), double_MEMBER(classify_character_fragments_garbage_certainty_threshold, -3.0, "Exclude fragments that do not look like whole" " characters from training and adaption", this->params()), BOOL_MEMBER(classify_debug_character_fragments, FALSE, "Bring up graphical debugging windows for fragments training", this->params()), BOOL_MEMBER(matcher_debug_separate_windows, FALSE, "Use two different windows for debugging the matching: " "One for the protos and one for the features.", this->params()), STRING_MEMBER(classify_learn_debug_str, "", "Class str to debug learning", this->params()), INT_MEMBER(classify_class_pruner_threshold, 229, "Class Pruner Threshold 0-255", this->params()), INT_MEMBER(classify_class_pruner_multiplier, 15, "Class Pruner Multiplier 0-255: ", this->params()), INT_MEMBER(classify_cp_cutoff_strength, 7, "Class Pruner CutoffStrength: ", this->params()), INT_MEMBER(classify_integer_matcher_multiplier, 10, "Integer Matcher Multiplier 0-255: ", this->params()), EnableLearning(true), INT_MEMBER(il1_adaption_test, 0, "Dont adapt to i/I at beginning of word", this->params()), BOOL_MEMBER(classify_bln_numeric_mode, 0, "Assume the input is numbers [0-9].", this->params()), double_MEMBER(speckle_large_max_size, 0.30, "Max large speckle size", this->params()), double_MEMBER(speckle_rating_penalty, 10.0, "Penalty to add to worst rating for noise", this->params()), shape_table_(NULL), dict_(this), static_classifier_(NULL) { fontinfo_table_.set_compare_callback( NewPermanentTessCallback(CompareFontInfo)); fontinfo_table_.set_clear_callback( NewPermanentTessCallback(FontInfoDeleteCallback)); fontset_table_.set_compare_callback( NewPermanentTessCallback(CompareFontSet)); fontset_table_.set_clear_callback( NewPermanentTessCallback(FontSetDeleteCallback)); AdaptedTemplates = NULL; PreTrainedTemplates = NULL; AllProtosOn = NULL; AllConfigsOn = NULL; AllConfigsOff = NULL; TempProtoMask = NULL; NormProtos = NULL; NumAdaptationsFailed = 0; learn_debug_win_ = NULL; learn_fragmented_word_debug_win_ = NULL; learn_fragments_debug_win_ = NULL; CharNormCutoffs = new uinT16[MAX_NUM_CLASSES]; BaselineCutoffs = new uinT16[MAX_NUM_CLASSES]; } Classify::~Classify() { EndAdaptiveClassifier(); delete learn_debug_win_; delete learn_fragmented_word_debug_win_; delete learn_fragments_debug_win_; delete[] CharNormCutoffs; delete[] BaselineCutoffs; } // Takes ownership of the given classifier, and uses it for future calls // to CharNormClassifier. void Classify::SetStaticClassifier(ShapeClassifier* static_classifier) { delete static_classifier_; static_classifier_ = static_classifier; } // Moved from speckle.cpp // Adds a noise classification result that is a bit worse than the worst // current result, or the worst possible result if no current results. void Classify::AddLargeSpeckleTo(int blob_length, BLOB_CHOICE_LIST *choices) { BLOB_CHOICE_IT bc_it(choices); // If there is no classifier result, we will use the worst possible certainty // and corresponding rating. float certainty = -getDict().certainty_scale; float rating = rating_scale * blob_length; if (!choices->empty() && blob_length > 0) { bc_it.move_to_last(); BLOB_CHOICE* worst_choice = bc_it.data(); // Add speckle_rating_penalty to worst rating, matching old value. rating = worst_choice->rating() + speckle_rating_penalty; // Compute the rating to correspond to the certainty. (Used to be kept // the same, but that messes up the language model search.) certainty = -rating * getDict().certainty_scale / (rating_scale * blob_length); } BLOB_CHOICE* blob_choice = new BLOB_CHOICE(UNICHAR_SPACE, rating, certainty, -1, -1, 0, 0, MAX_FLOAT32, 0, BCC_SPECKLE_CLASSIFIER); bc_it.add_to_end(blob_choice); } // Returns true if the blob is small enough to be a large speckle. bool Classify::LargeSpeckle(const TBLOB &blob) { double speckle_size = kBlnXHeight * speckle_large_max_size; TBOX bbox = blob.bounding_box(); return bbox.width() < speckle_size && bbox.height() < speckle_size; } } // namespace tesseract