/********************************************************************** * File: cube_object.cpp * Description: Implementation of the Cube Object Class * Author: Ahmad Abdulkader * Created: 2007 * * (C) Copyright 2008, Google Inc. ** Licensed under the Apache License, Version 2.0 (the "License"); ** you may not use this file except in compliance with the License. ** You may obtain a copy of the License at ** http://www.apache.org/licenses/LICENSE-2.0 ** Unless required by applicable law or agreed to in writing, software ** distributed under the License is distributed on an "AS IS" BASIS, ** WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ** See the License for the specific language governing permissions and ** limitations under the License. * **********************************************************************/ #include <math.h> #include "cube_object.h" #include "cube_utils.h" #include "word_list_lang_model.h" namespace tesseract { CubeObject::CubeObject(CubeRecoContext *cntxt, CharSamp *char_samp) { Init(); char_samp_ = char_samp; cntxt_ = cntxt; } CubeObject::CubeObject(CubeRecoContext *cntxt, Pix *pix, int left, int top, int wid, int hgt) { Init(); char_samp_ = CubeUtils::CharSampleFromPix(pix, left, top, wid, hgt); own_char_samp_ = true; cntxt_ = cntxt; } // Data member initialization function void CubeObject::Init() { char_samp_ = NULL; own_char_samp_ = false; alt_list_ = NULL; srch_obj_ = NULL; deslanted_alt_list_ = NULL; deslanted_srch_obj_ = NULL; deslanted_ = false; deslanted_char_samp_ = NULL; beam_obj_ = NULL; deslanted_beam_obj_ = NULL; cntxt_ = NULL; } // Cleanup function void CubeObject::Cleanup() { delete alt_list_; alt_list_ = NULL; delete deslanted_alt_list_; deslanted_alt_list_ = NULL; } CubeObject::~CubeObject() { if (own_char_samp_ == true) { delete char_samp_; char_samp_ = NULL; } delete srch_obj_; srch_obj_ = NULL; delete deslanted_srch_obj_; deslanted_srch_obj_ = NULL; delete beam_obj_; beam_obj_ = NULL; delete deslanted_beam_obj_; deslanted_beam_obj_ = NULL; delete deslanted_char_samp_; deslanted_char_samp_ = NULL; Cleanup(); } /** * Actually do the recognition using the specified language mode. If none * is specified, the default language model in the CubeRecoContext is used. * @return the sorted list of alternate answers * @param word_mode determines whether recognition is done as a word or a phrase */ WordAltList *CubeObject::Recognize(LangModel *lang_mod, bool word_mode) { if (char_samp_ == NULL) { return NULL; } // clear alt lists Cleanup(); // no specified language model, use the one in the reco context if (lang_mod == NULL) { lang_mod = cntxt_->LangMod(); } // normalize if necessary if (cntxt_->SizeNormalization()) { Normalize(); } // assume not de-slanted by default deslanted_ = false; // create a beam search object if (beam_obj_ == NULL) { beam_obj_ = new BeamSearch(cntxt_, word_mode); } // create a cube search object if (srch_obj_ == NULL) { srch_obj_ = new CubeSearchObject(cntxt_, char_samp_); } // run a beam search against the tesslang model alt_list_ = beam_obj_->Search(srch_obj_, lang_mod); // deslant (if supported by language) and re-reco if probability is low enough if (cntxt_->HasItalics() == true && (alt_list_ == NULL || alt_list_->AltCount() < 1 || alt_list_->AltCost(0) > CubeUtils::Prob2Cost(kMinProbSkipDeslanted))) { if (deslanted_beam_obj_ == NULL) { deslanted_beam_obj_ = new BeamSearch(cntxt_); } if (deslanted_srch_obj_ == NULL) { deslanted_char_samp_ = char_samp_->Clone(); if (deslanted_char_samp_ == NULL) { fprintf(stderr, "Cube ERROR (CubeObject::Recognize): could not " "construct deslanted CharSamp\n"); return NULL; } if (deslanted_char_samp_->Deslant() == false) { return NULL; } deslanted_srch_obj_ = new CubeSearchObject(cntxt_, deslanted_char_samp_); } // run a beam search against the tesslang model deslanted_alt_list_ = deslanted_beam_obj_->Search(deslanted_srch_obj_, lang_mod); // should we use de-slanted altlist? if (deslanted_alt_list_ != NULL && deslanted_alt_list_->AltCount() > 0) { if (alt_list_ == NULL || alt_list_->AltCount() < 1 || deslanted_alt_list_->AltCost(0) < alt_list_->AltCost(0)) { deslanted_ = true; return deslanted_alt_list_; } } } return alt_list_; } /** * Recognize the member char sample as a word */ WordAltList *CubeObject::RecognizeWord(LangModel *lang_mod) { return Recognize(lang_mod, true); } /** * Recognize the member char sample as a phrase */ WordAltList *CubeObject::RecognizePhrase(LangModel *lang_mod) { return Recognize(lang_mod, false); } /** * Computes the cost of a specific string. This is done by performing * recognition of a language model that allows only the specified word */ int CubeObject::WordCost(const char *str) { WordListLangModel *lang_mod = new WordListLangModel(cntxt_); if (lang_mod->AddString(str) == false) { delete lang_mod; return WORST_COST; } // run a beam search against the single string wordlist model WordAltList *alt_list = RecognizeWord(lang_mod); delete lang_mod; int cost = WORST_COST; if (alt_list != NULL) { if (alt_list->AltCount() > 0) { cost = alt_list->AltCost(0); } } return cost; } // Recognizes a single character and returns the list of results. CharAltList *CubeObject::RecognizeChar() { if (char_samp_ == NULL) return NULL; CharAltList* alt_list = NULL; CharClassifier *char_classifier = cntxt_->Classifier(); ASSERT_HOST(char_classifier != NULL); alt_list = char_classifier->Classify(char_samp_); return alt_list; } // Normalize the input word bitmap to have a minimum aspect ratio bool CubeObject::Normalize() { // create a cube search object CubeSearchObject *srch_obj = new CubeSearchObject(cntxt_, char_samp_); // Perform over-segmentation int seg_cnt = srch_obj->SegPtCnt(); // Only perform normalization if segment count is large enough if (seg_cnt < kMinNormalizationSegmentCnt) { delete srch_obj; return true; } // compute the mean AR of the segments double ar_mean = 0.0; for (int seg_idx = 0; seg_idx <= seg_cnt; seg_idx++) { CharSamp *seg_samp = srch_obj->CharSample(seg_idx - 1, seg_idx); if (seg_samp != NULL && seg_samp->Width() > 0) { ar_mean += (1.0 * seg_samp->Height() / seg_samp->Width()); } } ar_mean /= (seg_cnt + 1); // perform normalization if segment AR is too high if (ar_mean > kMinNormalizationAspectRatio) { // scale down the image in the y-direction to attain AR CharSamp *new_samp = char_samp_->Scale(char_samp_->Width(), 2.0 * char_samp_->Height() / ar_mean, false); if (new_samp != NULL) { // free existing char samp if owned if (own_char_samp_) { delete char_samp_; } // update with new scaled charsamp and set ownership flag char_samp_ = new_samp; own_char_samp_ = true; } } delete srch_obj; return true; } }