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git-svn-id: https://tesseract-ocr.googlecode.com/svn/trunk@653 d0cd1f9f-072b-0410-8dd7-cf729c803f20
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| /////////////////////////////////////////////////////////////////////// | ||
| // File: ambiguous_words.cpp | ||
| // Description: A program that takes a text file with a list of words as | ||
| // input (one per line) and outputs a file with the words | ||
| // that were found in the dictionary followed by the words | ||
| // that are ambiguous to them. | ||
| // Author: Rika Antonova | ||
| // Created: Fri Oct 21 11:26:43 PDT 2011 | ||
| // | ||
| // (C) Copyright 2011, 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. | ||
| // | ||
| /////////////////////////////////////////////////////////////////////// | ||
| // | ||
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| #include <stdio.h> | ||
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| #include "baseapi.h" | ||
| #include "helpers.h" | ||
| #include "strngs.h" | ||
| #include "dict.h" | ||
| #include "tesseractclass.h" | ||
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| int main(int argc, char** argv) { | ||
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| // Parse input arguments. | ||
| if (argc != 4 && (argc != 6 || strcmp(argv[1], "-l") != 0)) { | ||
| printf("Usage: %s [-l lang] tessdata_dir wordlist_file" | ||
| " output_ambiguious_wordlist_file\n", argv[0]); | ||
| return 1; | ||
| } | ||
| int argv_offset = 0; | ||
| STRING lang; | ||
| if (argc == 6) { | ||
| lang = argv[2]; | ||
| argv_offset = 2; | ||
| } else { | ||
| lang = "eng"; | ||
| } | ||
| const char *tessdata_dir = argv[++argv_offset]; | ||
| const char *input_file_str = argv[++argv_offset]; | ||
| const char *output_file_str = argv[++argv_offset]; | ||
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| // Initialize Tesseract. | ||
| tesseract::TessBaseAPI api; | ||
| GenericVector<STRING> vars_vec; | ||
| GenericVector<STRING> vars_values; | ||
| vars_vec.push_back("output_ambig_words_file"); | ||
| vars_values.push_back(output_file_str); | ||
| api.Init(tessdata_dir, lang.string(), tesseract::OEM_TESSERACT_ONLY, | ||
| NULL, NULL, &vars_vec, &vars_values, false); | ||
| tesseract::Dict &dict = api.tesseract()->getDict(); | ||
| FILE *input_file = fopen(input_file_str, "r"); | ||
| if (input_file == NULL) { | ||
| tprintf("Failed to open input wordlist file %s\n", input_file_str); | ||
| exit(1); | ||
| } | ||
| char str[CHARS_PER_LINE]; | ||
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| // Read word list and call Dict::NoDangerousAmbig() for each word | ||
| // to record ambiguities in the output file. | ||
| while (fgets(str, CHARS_PER_LINE, input_file) != NULL) { | ||
| chomp_string(str); // remove newline | ||
| WERD_CHOICE word(str, dict.getUnicharset()); | ||
| dict.NoDangerousAmbig(&word, NULL, false, NULL, NULL); | ||
| } | ||
| // Clean up. | ||
| fclose(input_file); | ||
| } |
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| // Copyright 2011 Google Inc. All Rights Reserved. | ||
| // Author: rays@google.com (Ray Smith) | ||
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| // 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. | ||
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| // Filename: classifier_tester.cpp | ||
| // Purpose: Tests a character classifier on data as formatted for training, | ||
| // but doesn't have to be the same as the training data. | ||
| // Author: Ray Smith | ||
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| #ifndef USE_STD_NAMESPACE | ||
| #include "base/commandlineflags.h" | ||
| #endif | ||
| #include "baseapi.h" | ||
| #include "commontraining.h" | ||
| #include "cubeclassifier.h" | ||
| #include "mastertrainer.h" | ||
| #include "params.h" | ||
| #include "strngs.h" | ||
| #include "tessclassifier.h" | ||
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| STRING_PARAM_FLAG(classifier, "", "Classifier to test"); | ||
| STRING_PARAM_FLAG(lang, "eng", "Language to test"); | ||
| STRING_PARAM_FLAG(tessdata_dir, "", "Directory of traineddata files"); | ||
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| enum ClassifierName { | ||
| CN_PRUNER, | ||
| CN_FULL, | ||
| CN_CUBE, | ||
| CN_CUBETESS, | ||
| CN_COUNT | ||
| }; | ||
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| const char* names[] = {"pruner", "full", "cube", "cubetess", NULL }; | ||
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| // This program has complex setup requirements, so here is some help: | ||
| // Two different modes, tr files and serialized mastertrainer. | ||
| // From tr files: | ||
| // classifier_tester -U unicharset -F font_properties -X xheights | ||
| // -classifier x -lang lang [-output_trainer trainer] *.tr | ||
| // From a serialized trainer: | ||
| // classifier_tester -input_trainer trainer [-lang lang] -classifier x | ||
| // | ||
| // In the first case, the unicharset must be the unicharset from within | ||
| // the classifier under test, and the font_properties and xheights files must | ||
| // match the files used during training. | ||
| // In the second case, the trainer file must have been prepared from | ||
| // some previous run of shapeclustering, mftraining, or classifier_tester | ||
| // using the same conditions as above, ie matching unicharset/font_properties. | ||
| // | ||
| // Available values of classifier (x above) are: | ||
| // pruner : Tesseract class pruner only. | ||
| // full : Tesseract full classifier. | ||
| // cube : Cube classifier. (Not possible with an input trainer.) | ||
| // cubetess : Tesseract class pruner with rescoring by Cube. (Not possible | ||
| // with an input trainer.) | ||
| int main(int argc, char **argv) { | ||
| ParseArguments(&argc, &argv); | ||
| // Decode the classifier string. | ||
| ClassifierName classifier = CN_COUNT; | ||
| for (int c = 0; c < CN_COUNT; ++c) { | ||
| if (strcmp(FLAGS_classifier.c_str(), names[c]) == 0) { | ||
| classifier = static_cast<ClassifierName>(c); | ||
| break; | ||
| } | ||
| } | ||
| if (classifier == CN_COUNT) { | ||
| fprintf(stderr, "Invalid classifier name:%s\n", FLAGS_classifier.c_str()); | ||
| return 1; | ||
| } | ||
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| STRING file_prefix; | ||
| tesseract::MasterTrainer* trainer = tesseract::LoadTrainingData( | ||
| argc, argv, true, NULL, &file_prefix); | ||
| // We want to test junk as well if it is available. | ||
| trainer->IncludeJunk(); | ||
| // We want to test with replicated samples too. | ||
| trainer->ReplicateAndRandomizeSamplesIfRequired(); | ||
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| // We need to initialize tesseract to test. | ||
| tesseract::TessBaseAPI api; | ||
| tesseract::OcrEngineMode engine_mode = tesseract::OEM_TESSERACT_ONLY; | ||
| if (classifier == CN_CUBE || classifier == CN_CUBETESS) | ||
| engine_mode = tesseract::OEM_TESSERACT_CUBE_COMBINED; | ||
| if (api.Init(FLAGS_tessdata_dir.c_str(), FLAGS_lang.c_str(), | ||
| engine_mode) < 0) { | ||
| fprintf(stderr, "Tesseract initialization failed!\n"); | ||
| return 1; | ||
| } | ||
| tesseract::ShapeClassifier* shape_classifier = NULL; | ||
| tesseract::Tesseract* tesseract = | ||
| const_cast<tesseract::Tesseract*>(api.tesseract()); | ||
| tesseract::Classify* classify = | ||
| reinterpret_cast<tesseract::Classify*>(tesseract); | ||
| // Copy the shape_table from the classifier and add the space character if | ||
| // not already present to count junk. | ||
| tesseract::ShapeTable shape_table; | ||
| shape_table.set_unicharset(classify->shape_table()->unicharset()); | ||
| shape_table.AppendMasterShapes(*classify->shape_table()); | ||
| if (shape_table.FindShape(0, -1) < 0) | ||
| shape_table.AddShape(0, 0); | ||
| if (classifier == CN_PRUNER) { | ||
| shape_classifier = new tesseract::TessClassifier(true, classify); | ||
| } else if (classifier == CN_FULL) { | ||
| shape_classifier = new tesseract::TessClassifier(false, classify); | ||
| } else if (classifier == CN_CUBE) { | ||
| shape_classifier = new tesseract::CubeClassifier(tesseract); | ||
| } else if (classifier == CN_CUBETESS) { | ||
| shape_classifier = new tesseract::CubeTessClassifier(tesseract); | ||
| } else { | ||
| fprintf(stderr, "%s tester not yet implemented\n", | ||
| FLAGS_classifier.c_str()); | ||
| return 1; | ||
| } | ||
| tprintf("Testing classifier %s:\n", FLAGS_classifier.c_str()); | ||
| trainer->TestClassifierOnSamples(3, false, shape_classifier, NULL); | ||
| if (classifier != CN_CUBE && classifier != CN_CUBETESS) { | ||
| // Test with replicated samples as well. | ||
| trainer->TestClassifierOnSamples(3, true, shape_classifier, NULL); | ||
| } | ||
| delete shape_classifier; | ||
| delete trainer; | ||
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| return 0; | ||
| } /* main */ | ||
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