56 lines
1.9 KiB
C++
56 lines
1.9 KiB
C++
/*
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* cropped_word_recognition.cpp
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*
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* A demo program of text recognition in a given cropped word.
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* Shows the use of the OCRBeamSearchDecoder class API using the provided default classifier.
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*
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* Created on: Jul 9, 2015
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* Author: Lluis Gomez i Bigorda <lgomez AT cvc.uab.es>
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*/
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#include "opencv2/text.hpp"
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#include "opencv2/core/utility.hpp"
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#include "opencv2/highgui.hpp"
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#include "opencv2/imgproc.hpp"
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#include <iostream>
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using namespace std;
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using namespace cv;
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using namespace cv::text;
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int main(int argc, char* argv[])
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{
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cout << endl << argv[0] << endl << endl;
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cout << "A demo program of Scene Text Character Recognition: " << endl;
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cout << "Shows the use of the OCRBeamSearchDecoder::ClassifierCallback class using the Single Layer CNN character classifier described in:" << endl;
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cout << "Coates, Adam, et al. \"Text detection and character recognition in scene images with unsupervised feature learning.\" ICDAR 2011." << endl << endl;
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Mat image;
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if(argc>1)
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image = imread(argv[1]);
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else
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{
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cout << " Usage: " << argv[0] << " <input_image>" << endl;
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cout << " the input image must contain a single character (e.g. scenetext_char01.jpg)." << endl << endl;
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return(0);
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}
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string vocabulary = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789"; // must have the same order as the classifier output classes
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Ptr<OCRHMMDecoder::ClassifierCallback> ocr = loadOCRHMMClassifierCNN("OCRBeamSearch_CNN_model_data.xml.gz");
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double t_r = (double)getTickCount();
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vector<int> out_classes;
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vector<double> out_confidences;
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ocr->eval(image, out_classes, out_confidences);
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cout << "OCR output = \"" << vocabulary[out_classes[0]] << "\" with confidence "
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<< out_confidences[0] << ". Evaluated in "
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<< ((double)getTickCount() - t_r)*1000/getTickFrequency() << " ms." << endl << endl;
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return 0;
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}
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