From 7cd6dbb0c9b9aa6a037a8090e6ebc96a79cc9092 Mon Sep 17 00:00:00 2001 From: Lluis Gomez-Bigorda Date: Wed, 26 Aug 2015 20:13:15 +0200 Subject: [PATCH] Adds two more examples of croped word recognition, and show how to set the beam size parameter for small lexicon. --- .../text/samples/cropped_word_recognition.cpp | 9 ++++++--- modules/text/samples/scenetext_word03.jpg | Bin 0 -> 17533 bytes modules/text/samples/scenetext_word04.jpg | Bin 0 -> 47475 bytes 3 files changed, 6 insertions(+), 3 deletions(-) create mode 100644 modules/text/samples/scenetext_word03.jpg create mode 100644 modules/text/samples/scenetext_word04.jpg diff --git a/modules/text/samples/cropped_word_recognition.cpp b/modules/text/samples/cropped_word_recognition.cpp index 65ac792f1..32e3570e5 100644 --- a/modules/text/samples/cropped_word_recognition.cpp +++ b/modules/text/samples/cropped_word_recognition.cpp @@ -39,12 +39,13 @@ int main(int argc, char* argv[]) string vocabulary = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789"; // must have the same order as the clasifier output classes vector lexicon; // a list of words expected to be found on the input image lexicon.push_back(string("abb")); - lexicon.push_back(string("patata")); + lexicon.push_back(string("riser")); lexicon.push_back(string("CHINA")); lexicon.push_back(string("HERE")); lexicon.push_back(string("President")); lexicon.push_back(string("smash")); lexicon.push_back(string("KUALA")); + lexicon.push_back(string("Produkt")); lexicon.push_back(string("NINTENDO")); // Create tailored language model a small given lexicon @@ -54,16 +55,18 @@ int main(int argc, char* argv[]) // An alternative would be to load the default generic language model // (created from ispell 42869 english words list) /*Mat transition_p; - string filename = "OCRHMM_transitions_table.xml"; // TODO use same order for voc + string filename = "OCRHMM_transitions_table.xml"; FileStorage fs(filename, FileStorage::READ); fs["transition_probabilities"] >> transition_p; fs.release();*/ Mat emission_p = Mat::eye(62,62,CV_64FC1); + // Notice we set here a beam size of 50. This is much faster than using the default value (500). + // 50 works well with our tiny lexicon example, but may not with larger dictionaries. Ptr ocr = OCRBeamSearchDecoder::create( loadOCRBeamSearchClassifierCNN("OCRBeamSearch_CNN_model_data.xml.gz"), - vocabulary, transition_p, emission_p); + vocabulary, transition_p, emission_p, OCR_DECODER_VITERBI, 50); double t_r = (double)getTickCount(); string output; diff --git a/modules/text/samples/scenetext_word03.jpg b/modules/text/samples/scenetext_word03.jpg new file mode 100644 index 0000000000000000000000000000000000000000..7d1753c601d47c7e8f75487acba0a4a5d008ae3a GIT binary patch literal 17533 zcmch81yodD*Z0s3LpKiHFm!h#APv$rbTA=3B_27jOT zdEW2))_T`^@2t!DopXMBpW0{dbMFj4zWi7M02O5wWC1WRFaUYz2k>JVAx*~H#u5Nf zP+$Te0{{Rt045A900c!z&>IVifzb3V^aesP3N&qp-Z(ID02pX`cay*%{7n0ynDjRu zhhm1`99SskfXbpl(>dtP3B`EObPIap!v4~8$FYfk|EcF1itjFE1(ioini3#s1vv#x z2_=w}gtCmf1nAFoUoZ8DjYUaQ8X#?>EFmWY(goZN?5CzbshoncDu|PfpN$jRI+%k; z5X>*g$qC}%7v$m-1oHxj0GYwR#y|z_NX~D}1jSU5|MZXkH@3LzTmljR9-4qTIlyoU zzsAD}<-`BRvQP|@_=^Jr9V2YwFa3IVoFvqry)%Pigrr~Pwor_e^s6lds9xmX_^v6G 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