Merge pull request #344 from lluisgomez:ocr_character_recognizer

modules/datasets/samples/tr_chars_benchmark.cpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
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+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
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+//
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+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
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+//     derived from this software without specific prior written permission.
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+// In no event shall the Itseez Inc or contributors be liable for any direct,
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+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
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+//M*/
+
+#include "opencv2/datasets/tr_chars.hpp"
+
+#include <opencv2/core.hpp>
+
+#include "opencv2/text.hpp"
+#include "opencv2/imgproc.hpp"
+#include "opencv2/imgcodecs.hpp"
+
+#include <cstdio>
+#include <cstdlib> // atoi
+
+#include <string>
+#include <vector>
+
+using namespace std;
+using namespace cv;
+using namespace cv::datasets;
+using namespace cv::text;
+
+int main(int argc, char *argv[])
+{
+    const char *keys =
+            "{ help h usage ? |    | show this message }"
+            "{ path p         |true| path to dataset description file ( list_English_Img.m ) and Img folder.}";
+    CommandLineParser parser(argc, argv, keys);
+    string path(parser.get<string>("path"));
+    if (parser.has("help") || path=="true")
+    {
+        parser.printMessage();
+        return -1;
+    }
+
+    Ptr<TR_chars> dataset = TR_chars::create();
+    dataset->load(path);
+
+    // ***************
+    // dataset. train, test contain information about each element of appropriate sets and splits.
+    // For example, let output first elements of these vectors and their sizes for last split.
+    // And number of splits.
+    int numSplits = dataset->getNumSplits();
+    printf("splits number: %u\n", numSplits);
+
+    vector< Ptr<Object> > &currTrain = dataset->getTrain(numSplits-1);
+    vector< Ptr<Object> > &currTest = dataset->getTest(numSplits-1);
+    vector< Ptr<Object> > &currValidation = dataset->getValidation(numSplits-1);
+    printf("train size: %u\n", (unsigned int)currTrain.size());
+    printf("test size: %u\n", (unsigned int)currTest.size());
+    printf("validation size: %u\n", (unsigned int)currValidation.size());
+
+
+    // WARNING: The order of classes' labels is different in Chars74k and in the output of our classifier
+    string src_classes = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789"; // labels order as in the clasifier output
+    string tar_classes = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"; // labels order as in the Chars74k dataset
+
+    Ptr<OCRHMMDecoder::ClassifierCallback> ocr = loadOCRHMMClassifierCNN("OCRBeamSearch_CNN_model_data.xml.gz");
+
+    int numOK = 0;
+    int upperNumOK = 0;
+
+    for (unsigned int i=0; i<(unsigned int)currTest.size(); i++)
+    {
+        TR_charsObj *exampleTest = static_cast<TR_charsObj *>(currTest[i].get());
+        printf("processed image: %u, name: %s\n", i, exampleTest->imgName.c_str());
+        printf("  label: %u,", exampleTest->label);
+
+        string imfilename = path+string("/Img/")+exampleTest->imgName.c_str()+string(".png");
+        Mat image  = imread(imfilename);
+        vector<int> out_classes;
+        vector<double> out_confidences;
+        ocr->eval(image, out_classes, out_confidences);
+        int prediction = 1 + tar_classes.find_first_of(src_classes[out_classes[0]]);
+        printf(" prediction: %u\n", prediction);
+
+        if (exampleTest->label == prediction)
+            numOK++;
+
+        char l = tar_classes[exampleTest->label];
+        char p = tar_classes[prediction];
+        if (toupper(l) == toupper(p))
+            upperNumOK++;
+    }
+
+    printf("\n---------------------------------------------\n");
+    printf("Chars74k Classification Accuracy (case-sensitive): %f\n",(float)numOK/currTest.size());
+    printf("Chars74k Classification Accuracy (case-insensitive): %f\n",(float)upperNumOK/currTest.size());
+
+    return 0;
+}

modules/text/samples/character_recognition.cpp

+/*
+ * 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 clasifier 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;
+}