[image-processing] image processing to improve tesseract OCR accuracy

I've been using tesseract to convert documents into text. The quality of the documents ranges wildly, and I'm looking for tips on what sort of image processing might improve the results. I've noticed that text that is highly pixellated - for example that generated by fax machines - is especially difficult for tesseract to process - presumably all those jagged edges to the characters confound the shape-recognition algorithms.

What sort of image processing techniques would improve the accuracy? I've been using a Gaussian blur to smooth out the pixellated images and seen some small improvement, but I'm hoping that there is a more specific technique that would yield better results. Say a filter that was tuned to black and white images, which would smooth out irregular edges, followed by a filter which would increase the contrast to make the characters more distinct.

Any general tips for someone who is a novice at image processing?

This question is related to image-processing ocr tesseract

The answer is

Three points to improve the readability of the image:

  1. Resize the image with variable height and width(multiply 0.5 and 1 and 2 with image height and width).

  2. Convert the image to Gray scale format(Black and white).

  3. Remove the noise pixels and make more clear(Filter the image).

Refer below code :


public Bitmap Resize(Bitmap bmp, int newWidth, int newHeight)
                Bitmap temp = (Bitmap)bmp;
                Bitmap bmap = new Bitmap(newWidth, newHeight, temp.PixelFormat);
                double nWidthFactor = (double)temp.Width / (double)newWidth;
                double nHeightFactor = (double)temp.Height / (double)newHeight;

                double fx, fy, nx, ny;
                int cx, cy, fr_x, fr_y;
                Color color1 = new Color();
                Color color2 = new Color();
                Color color3 = new Color();
                Color color4 = new Color();
                byte nRed, nGreen, nBlue;

                byte bp1, bp2;

                for (int x = 0; x < bmap.Width; ++x)
                    for (int y = 0; y < bmap.Height; ++y)

                        fr_x = (int)Math.Floor(x * nWidthFactor);
                        fr_y = (int)Math.Floor(y * nHeightFactor);
                        cx = fr_x + 1;
                        if (cx >= temp.Width) cx = fr_x;
                        cy = fr_y + 1;
                        if (cy >= temp.Height) cy = fr_y;
                        fx = x * nWidthFactor - fr_x;
                        fy = y * nHeightFactor - fr_y;
                        nx = 1.0 - fx;
                        ny = 1.0 - fy;

                        color1 = temp.GetPixel(fr_x, fr_y);
                        color2 = temp.GetPixel(cx, fr_y);
                        color3 = temp.GetPixel(fr_x, cy);
                        color4 = temp.GetPixel(cx, cy);

                        // Blue
                        bp1 = (byte)(nx * color1.B + fx * color2.B);

                        bp2 = (byte)(nx * color3.B + fx * color4.B);

                        nBlue = (byte)(ny * (double)(bp1) + fy * (double)(bp2));

                        // Green
                        bp1 = (byte)(nx * color1.G + fx * color2.G);

                        bp2 = (byte)(nx * color3.G + fx * color4.G);

                        nGreen = (byte)(ny * (double)(bp1) + fy * (double)(bp2));

                        // Red
                        bp1 = (byte)(nx * color1.R + fx * color2.R);

                        bp2 = (byte)(nx * color3.R + fx * color4.R);

                        nRed = (byte)(ny * (double)(bp1) + fy * (double)(bp2));

                        bmap.SetPixel(x, y, System.Drawing.Color.FromArgb
                (255, nRed, nGreen, nBlue));


                bmap = SetGrayscale(bmap);
                bmap = RemoveNoise(bmap);

                return bmap;


public Bitmap SetGrayscale(Bitmap img)
                Bitmap temp = (Bitmap)img;
                Bitmap bmap = (Bitmap)temp.Clone();
                Color c;
                for (int i = 0; i < bmap.Width; i++)
                    for (int j = 0; j < bmap.Height; j++)
                        c = bmap.GetPixel(i, j);
                        byte gray = (byte)(.299 * c.R + .587 * c.G + .114 * c.B);
                        bmap.SetPixel(i, j, Color.FromArgb(gray, gray, gray));
                return (Bitmap)bmap.Clone();


public Bitmap RemoveNoise(Bitmap bmap)
                for (var x = 0; x < bmap.Width; x++)
                    for (var y = 0; y < bmap.Height; y++)
                        var pixel = bmap.GetPixel(x, y);
                        if (pixel.R < 162 && pixel.G < 162 && pixel.B < 162)
                            bmap.SetPixel(x, y, Color.Black);
                        else if (pixel.R > 162 && pixel.G > 162 && pixel.B > 162)
                            bmap.SetPixel(x, y, Color.White);
                return bmap;



The Tesseract documentation contains some good details on how to improve the OCR quality via image processing steps.

To some degree, Tesseract automatically applies them. It is also possible to tell Tesseract to write an intermediate image for inspection, i.e. to check how well the internal image processing works (search for tessedit_write_images in the above reference).

More importantly, the new neural network system in Tesseract 4 yields much better OCR results - in general and especially for images with some noise. It is enabled with --oem 1, e.g. as in:

$ tesseract --oem 1 -l deu page.png result pdf

(this example selects the german language)

Thus, it makes sense to test first how far you get with the new Tesseract LSTM mode before applying some custom pre-processing image processing steps.

Java version for Sathyaraj's code above:

// Resize
public Bitmap resize(Bitmap img, int newWidth, int newHeight) {
    Bitmap bmap = img.copy(img.getConfig(), true);

    double nWidthFactor = (double) img.getWidth() / (double) newWidth;
    double nHeightFactor = (double) img.getHeight() / (double) newHeight;

    double fx, fy, nx, ny;
    int cx, cy, fr_x, fr_y;
    int color1;
    int color2;
    int color3;
    int color4;
    byte nRed, nGreen, nBlue;

    byte bp1, bp2;

    for (int x = 0; x < bmap.getWidth(); ++x) {
        for (int y = 0; y < bmap.getHeight(); ++y) {

            fr_x = (int) Math.floor(x * nWidthFactor);
            fr_y = (int) Math.floor(y * nHeightFactor);
            cx = fr_x + 1;
            if (cx >= img.getWidth())
                cx = fr_x;
            cy = fr_y + 1;
            if (cy >= img.getHeight())
                cy = fr_y;
            fx = x * nWidthFactor - fr_x;
            fy = y * nHeightFactor - fr_y;
            nx = 1.0 - fx;
            ny = 1.0 - fy;

            color1 = img.getPixel(fr_x, fr_y);
            color2 = img.getPixel(cx, fr_y);
            color3 = img.getPixel(fr_x, cy);
            color4 = img.getPixel(cx, cy);

            // Blue
            bp1 = (byte) (nx * Color.blue(color1) + fx * Color.blue(color2));
            bp2 = (byte) (nx * Color.blue(color3) + fx * Color.blue(color4));
            nBlue = (byte) (ny * (double) (bp1) + fy * (double) (bp2));

            // Green
            bp1 = (byte) (nx * Color.green(color1) + fx * Color.green(color2));
            bp2 = (byte) (nx * Color.green(color3) + fx * Color.green(color4));
            nGreen = (byte) (ny * (double) (bp1) + fy * (double) (bp2));

            // Red
            bp1 = (byte) (nx * Color.red(color1) + fx * Color.red(color2));
            bp2 = (byte) (nx * Color.red(color3) + fx * Color.red(color4));
            nRed = (byte) (ny * (double) (bp1) + fy * (double) (bp2));

            bmap.setPixel(x, y, Color.argb(255, nRed, nGreen, nBlue));

    bmap = setGrayscale(bmap);
    bmap = removeNoise(bmap);

    return bmap;

// SetGrayscale
private Bitmap setGrayscale(Bitmap img) {
    Bitmap bmap = img.copy(img.getConfig(), true);
    int c;
    for (int i = 0; i < bmap.getWidth(); i++) {
        for (int j = 0; j < bmap.getHeight(); j++) {
            c = bmap.getPixel(i, j);
            byte gray = (byte) (.299 * Color.red(c) + .587 * Color.green(c)
                    + .114 * Color.blue(c));

            bmap.setPixel(i, j, Color.argb(255, gray, gray, gray));
    return bmap;

// RemoveNoise
private Bitmap removeNoise(Bitmap bmap) {
    for (int x = 0; x < bmap.getWidth(); x++) {
        for (int y = 0; y < bmap.getHeight(); y++) {
            int pixel = bmap.getPixel(x, y);
            if (Color.red(pixel) < 162 && Color.green(pixel) < 162 && Color.blue(pixel) < 162) {
                bmap.setPixel(x, y, Color.BLACK);
    for (int x = 0; x < bmap.getWidth(); x++) {
        for (int y = 0; y < bmap.getHeight(); y++) {
            int pixel = bmap.getPixel(x, y);
            if (Color.red(pixel) > 162 && Color.green(pixel) > 162 && Color.blue(pixel) > 162) {
                bmap.setPixel(x, y, Color.WHITE);
    return bmap;

As a rule of thumb, I usually apply the following image pre-processing techniques using OpenCV library:

  1. Rescaling the image (it's recommended if you’re working with images that have a DPI of less than 300 dpi):

    img = cv2.resize(img, None, fx=1.2, fy=1.2, interpolation=cv2.INTER_CUBIC)
  2. Converting image to grayscale:

    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
  3. Applying dilation and erosion to remove the noise (you may play with the kernel size depending on your data set):

    kernel = np.ones((1, 1), np.uint8)
    img = cv2.dilate(img, kernel, iterations=1)
    img = cv2.erode(img, kernel, iterations=1)
  4. Applying blur, which can be done by using one of the following lines (each of which has its pros and cons, however, median blur and bilateral filter usually perform better than gaussian blur.):

    cv2.threshold(cv2.GaussianBlur(img, (5, 5), 0), 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
    cv2.threshold(cv2.bilateralFilter(img, 5, 75, 75), 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
    cv2.threshold(cv2.medianBlur(img, 3), 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
    cv2.adaptiveThreshold(cv2.GaussianBlur(img, (5, 5), 0), 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 2)
    cv2.adaptiveThreshold(cv2.bilateralFilter(img, 9, 75, 75), 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 2)
    cv2.adaptiveThreshold(cv2.medianBlur(img, 3), 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 2)

I've recently written a pretty simple guide to Tesseract but it should enable you to write your first OCR script and clear up some hurdles that I experienced when things were less clear than I would have liked in the documentation.

In case you'd like to check them out, here I'm sharing the links with you:

What was EXTREMLY HELPFUL to me on this way are the source codes for Capture2Text project. http://sourceforge.net/projects/capture2text/files/Capture2Text/.

BTW: Kudos to it's author for sharing such a painstaking algorithm.

Pay special attention to the file Capture2Text\SourceCode\leptonica_util\leptonica_util.c - that's the essence of image preprocession for this utility.

If you will run the binaries, you can check the image transformation before/after the process in Capture2Text\Output\ folder.

P.S. mentioned solution uses Tesseract for OCR and Leptonica for preprocessing.

Text Recognition depends on a variety of factors to produce a good quality output. OCR output highly depends on the quality of input image. This is why every OCR engine provides guidelines regarding the quality of input image and its size. These guidelines help OCR engine to produce accurate results.

I have written a detailed article on image processing in python. Kindly follow the link below for more explanation. Also added the python source code to implement those process.

Please write a comment if you have a suggestion or better idea on this topic to improve it.


I did these to get good results out of an image which has not very small text.

  1. Apply blur to the original image.
  2. Apply Adaptive Threshold.
  3. Apply Sharpening effect.

And if the still not getting good results, scale the image to 150% or 200%.

Reading text from image documents using any OCR engine have many issues in order get good accuracy. There is no fixed solution to all the cases but here are a few things which should be considered to improve OCR results.

1) Presence of noise due to poor image quality / unwanted elements/blobs in the background region. This requires some pre-processing operations like noise removal which can be easily done using gaussian filter or normal median filter methods. These are also available in OpenCV.

2) Wrong orientation of image: Because of wrong orientation OCR engine fails to segment the lines and words in image correctly which gives the worst accuracy.

3) Presence of lines: While doing word or line segmentation OCR engine sometimes also tries to merge the words and lines together and thus processing wrong content and hence giving wrong results. There are other issues also but these are the basic ones.

This post OCR application is an example case where some image pre-preocessing and post processing on OCR result can be applied to get better OCR accuracy.

you can do noise reduction and then apply thresholding, but that you can you can play around with the configuration of the OCR by changing the --psm and --oem values

try: --psm 5 --oem 2

you can also look at the following link for further details here

Adaptive thresholding is important if the lighting is uneven across the image. My preprocessing using GraphicsMagic is mentioned in this post: https://groups.google.com/forum/#!topic/tesseract-ocr/jONGSChLRv4

GraphicsMagic also has the -lat feature for Linear time Adaptive Threshold which I will try soon.

Another method of thresholding using OpenCV is described here: http://docs.opencv.org/trunk/doc/py_tutorials/py_imgproc/py_thresholding/py_thresholding.html

This is somewhat ago but it still might be useful.

My experience shows that resizing the image in-memory before passing it to tesseract sometimes helps.

Try different modes of interpolation. The post https://stackoverflow.com/a/4756906/146003 helped me a lot.

I am by no means an OCR expert. But I this week had need to convert text out of a jpg.

I started with a colorized, RGB 445x747 pixel jpg. I immediately tried tesseract on this, and the program converted almost nothing. I then went into GIMP and did the following. image>mode>grayscale image>scale image>1191x2000 pixels filters>enhance>unsharp mask with values of radius = 6.8, amount = 2.69, threshold = 0 I then saved as a new jpg at 100% quality.

Tesseract then was able to extract all the text into a .txt file

Gimp is your friend.

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