vendor: OpenCV 5.0.0 snapshot at 755e50675d97db9b7d449d8bd6b09888646f6c6e

2026-08-22 00:11:13 +08:00
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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 classifier 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;
+}