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i have to write a java program that recognizes all the letters in a picture taken with android camera .I tried KNN method but i could only recognize a single letter in the picture and i want to recognize all the letters .Can someonr help me. This is my code

public void train (){


    //Creating Training Data
     Mat trainData = new Mat(); 
     Mat train_labels = new Mat();

    for (int i =1; i <15; i++) {
        String path = Environment.getExternalStorageDirectory().toString() + "/Pictures/New Folder/" + i + ".png";

    Mat img =Imgcodecs.imread(path);
    Log.i(TAG,"error debug: "+i+img.empty());

    img.convertTo(img, CvType.CV_32FC1); // Convert  to float
    Size dsize = new Size(25,25); 
     Imgproc.resize(img, img, dsize); 

     img.convertTo(img, CvType.CV_32FC1);
     Mat imgResized = img.reshape(1, 1); //make continuous

     trainData.push_back(imgResized);

    train_labels.push_back(new Mat (1,1,CvType.CV_32FC1,new Scalar(i)));// add 1 item

    }

        Mat response=new Mat();
        Mat tmp;
        tmp= train_labels.reshape(1,1); //make continuous
        tmp.convertTo(response,CvType.CV_32FC1); // Convert  to float*/


     KNearest knn = KNearest.create();
     knn.train(trainData,Ml.ROW_SAMPLE, train_labels);




     //For Storing training data

 String path = Environment.getExternalStoragePublicDirectory(Environment.DIRECTORY_DOWNLOADS).toString() ;


Bitmap bmpOut1 = Bitmap.createBitmap(trainData.cols(), trainData.rows(), Bitmap.Config.ARGB_8888);
Bitmap bmpOut2 = Bitmap.createBitmap(response.cols(), response.rows(), Bitmap.Config.ARGB_8888);

response.convertTo(response,CvType.CV_8UC1);
trainData.convertTo(trainData,CvType.CV_8UC1);

Utils.matToBitmap(trainData, bmpOut1);
Utils.matToBitmap(response, bmpOut2);
//File file = new File(path);
// file.mkdirs();
File file = new File(path, "train.png");
File file2 = new File(path, "response.png");

OutputStream fout = null;
OutputStream fout2 = null;

try {
    fout = new FileOutputStream(file);
    fout2 = new FileOutputStream(file2);

    BufferedOutputStream bos = new BufferedOutputStream(fout);
    BufferedOutputStream bos2 = new BufferedOutputStream(fout2);

    bmpOut1.compress(Bitmap.CompressFormat.PNG, 100, bos);
    bos.flush();
    bos.close();
    bmpOut1.recycle();

    bmpOut2.compress(Bitmap.CompressFormat.PNG, 100, bos2);
    bos2.flush();
    bos2.close();
    bmpOut2.recycle();

} catch (FileNotFoundException e) {
    e.printStackTrace();
}

catch (IOException e) {
    e.printStackTrace();
}


 //For Accessing training data in BMP file  


    BitmapFactory.Options o = new BitmapFactory.Options();
    o.inScaled = false;
    Bitmap blankBitmap = BitmapFactory.decodeResource(getResources(), R.drawable.train,o);
    int trainWidth = blankBitmap.getWidth();
    int trainHeight = blankBitmap.getHeight();



    Mat trainData2 = new Mat();
    Utils.bitmapToMat(blankBitmap,trainData2);

    Bitmap blankBitmap2 = BitmapFactory.decodeResource(getResources(), R.drawable.response,o);
    int resWidth = blankBitmap2.getWidth();
    int resHeight = blankBitmap2.getHeight();

    Log.i(TAG,"res width "+ resWidth +" res Height"+ resHeight);
    Mat response2 = new Mat();
    Utils.bitmapToMat(blankBitmap2,response2);

    Log.i(TAG,"response2 width "+ response2.cols() +" res Height"+ response2.rows());

    Mat response3 = new Mat();
    Mat trainData3 = new Mat();

    Imgproc.cvtColor(response2,response2,Imgproc.COLOR_BGRA2GRAY);       // 1. change the number of channels
    Imgproc.cvtColor(trainData2,trainData2,Imgproc.COLOR_BGRA2GRAY);
    response2.convertTo(response3,CvType.CV_32FC1);
    trainData2.convertTo(trainData3,CvType.CV_32FC1);



    KNearest knn2 = KNearest.create();
    knn2.train(trainData3,Ml.ROW_SAMPLE, response3);


   Mat img = Imgcodecs.imread("/storage/emulated/0/DCIM/Camera/IMG.png");
   Log.i(TAG,"error1 img: "+img.empty());          
    Mat test = new Mat();
    Imgproc.resize(img,test, new Size(25,25) );
    test.convertTo(test, CvType.CV_32FC1);

    Mat results = new Mat();
    Mat responses = new Mat();
    Mat dists = new Mat();

    float label = knn.findNearest(test.reshape(1,1), 1,results,responses,dists);
    Log.i(TAG,"valeur "+label);

  if (1.0<=label &&  label <=8.0)

   {
       reply.setText("N");
   }
  else if (9.0<=label &&  label <=14.0){
       reply.setText("E");  
   }
  else
   reply.setText("rien");

}

}