Add benchmark code for the Chars74k dataset. Using the CNN character classifier…

Add benchmark code for the Chars74k dataset. Using the CNN character classifier reaches 75% and 84% accuracy for case-sensitive and case-insensitive recognition respectively.

modules/datasets/samples/tr_chars_benchmark.cpp

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+//                For Open Source Computer Vision Library
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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;
+}