OpenCV Adaptive Threshold OCR

Question

I am using OpenCV to prepare images for OCR from an iPhone camera, and I have been having trouble getting the results I need for an accurate OCR scan. Here is the code I am

Answer 1

JAVA CODE: A long time has passed since this question was made, but I've rewritten this code from C++ to Java in case someone will need it (I needed to use it for developing an app on android studio).

public Bitmap Thresholding(Bitmap bitmap)
{
    Mat imgMat = new Mat();
    Utils.bitmapToMat(bitmap, imgMat);
    imgMat.convertTo(imgMat, CvType.CV_32FC1, 1.0 / 255.0);

    Mat res = CalcBlockMeanVariance(imgMat, 21);
    Core.subtract(new MatOfDouble(1.0), res, res);
    Imgproc.cvtColor( imgMat, imgMat, Imgproc.COLOR_BGRA2BGR);
    Core.add(imgMat, res, res);

    Imgproc.threshold(res, res, 0.85, 1, Imgproc.THRESH_BINARY);

    res.convertTo(res, CvType.CV_8UC1, 255.0);
    Utils.matToBitmap(res, bitmap);

    return bitmap;
}

public Mat CalcBlockMeanVariance (Mat Img, int blockSide)
{
    Mat I = new Mat();
    Mat ResMat;
    Mat inpaintmask = new Mat();
    Mat patch;
    Mat smallImg = new Mat();
    MatOfDouble mean = new MatOfDouble();
    MatOfDouble stddev = new MatOfDouble();

    Img.convertTo(I, CvType.CV_32FC1);
    ResMat = Mat.zeros(Img.rows() / blockSide, Img.cols() / blockSide, CvType.CV_32FC1);

    for (int i = 0; i < Img.rows() - blockSide; i += blockSide)
    {
        for (int j = 0; j < Img.cols() - blockSide; j += blockSide)
        {
            patch = new Mat(I,new Rect(j,i, blockSide, blockSide));
            Core.meanStdDev(patch, mean, stddev);

            if (stddev.get(0,0)[0] > 0.01)
                ResMat.put(i / blockSide, j / blockSide, mean.get(0,0)[0]);
            else
                ResMat.put(i / blockSide, j / blockSide, 0);
        }
    }

    Imgproc.resize(I, smallImg, ResMat.size());
    Imgproc.threshold(ResMat, inpaintmask, 0.02, 1.0, Imgproc.THRESH_BINARY);

    Mat inpainted = new Mat();
    Imgproc.cvtColor(smallImg, smallImg, Imgproc.COLOR_RGBA2BGR);
    smallImg.convertTo(smallImg, CvType.CV_8UC1, 255.0);

    inpaintmask.convertTo(inpaintmask, CvType.CV_8UC1);
    Photo.inpaint(smallImg, inpaintmask, inpainted, 5, Photo.INPAINT_TELEA);

    Imgproc.resize(inpainted, ResMat, Img.size());
    ResMat.convertTo(ResMat, CvType.CV_32FC1, 1.0 / 255.0);

    return ResMat;
}