vendor: OpenCV 5.0.0 snapshot at 755e50675d97db9b7d449d8bd6b09888646f6c6e

modules/text/samples/character_recognition.cpp

@@ -0,0 +1,55 @@
+/*
+ * 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 Character Recognition: " << endl;
+    cout << "Shows the use of the OCRBeamSearchDecoder::ClassifierCallback 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;
+        cout << "           the input image must contain a single character (e.g. scenetext_char01.jpg)." << endl << endl;
+        return(0);
+    }
+
+    string vocabulary = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789"; // must have the same order as the classifier output classes
+
+    Ptr<OCRHMMDecoder::ClassifierCallback> ocr = loadOCRHMMClassifierCNN("OCRBeamSearch_CNN_model_data.xml.gz");
+
+    double t_r = (double)getTickCount();
+    vector<int> out_classes;
+    vector<double> out_confidences;
+
+    ocr->eval(image, out_classes, out_confidences);
+
+    cout << "OCR output = \"" << vocabulary[out_classes[0]] << "\" with confidence "
+         << out_confidences[0] << ". Evaluated in "
+         << ((double)getTickCount() - t_r)*1000/getTickFrequency() << " ms." << endl << endl;
+
+    return 0;
+}