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/*
* cropped_word_recognition.cpp
*
* A demo program of text recognition in a given cropped word.
* Shows the use of the OCRBeamSearchDecoder class API using the provided default classifier.
*
* Created on: Jul 9, 2015
* Author: Lluis Gomez i Bigorda <lgomez AT cvc.uab.es>
*/
#include "opencv2/text.hpp"
#include "opencv2/core/utility.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include <iostream>
using namespace std;
using namespace cv;
using namespace cv::text;
int main(int argc, char* argv[])
{
cout << endl << argv[0] << endl << endl;
cout << "A demo program of Scene Text cropped word Recognition: " << endl;
cout << "Shows the use of the OCRBeamSearchDecoder class using the Single Layer CNN character classifier described in:" << endl;
cout << "Coates, Adam, et al. \"Text detection and character recognition in scene images with unsupervised feature learning.\" ICDAR 2011." << endl << endl;
Mat image;
if(argc>1)
image = imread(argv[1]);
else
{
cout << " Usage: " << argv[0] << " <input_image>" << endl << endl;
return(0);
}
string vocabulary = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789"; // must have the same order as the clasifier output classes
vector<string> lexicon; // a list of words expected to be found on the input image
lexicon.push_back(string("abb"));
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
Mat transition_p;
createOCRHMMTransitionsTable(vocabulary,lexicon,transition_p);
// 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";
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<OCRBeamSearchDecoder> ocr = OCRBeamSearchDecoder::create(
loadOCRBeamSearchClassifierCNN("OCRBeamSearch_CNN_model_data.xml.gz"),
vocabulary, transition_p, emission_p, OCR_DECODER_VITERBI, 50);
double t_r = (double)getTickCount();
string output;
vector<Rect> boxes;
vector<string> words;
vector<float> confidences;
ocr->run(image, output, &boxes, &words, &confidences, OCR_LEVEL_WORD);
cout << "OCR output = \"" << output << "\". Decoded in "
<< ((double)getTickCount() - t_r)*1000/getTickFrequency() << " ms." << endl << endl;
return 0;
}
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