清理验证码图像

Question

I'm trying to clean up the image above I've tried several different methods using open cv, I either erode the original image too much to the point where parts of the letters become missing such as below:

I'm not really sure sure how to get rid of the last diagonal line and repair the S, my code so far is:

import cv2 
import matplotlib.pylab as plt
img = cv2.imread('/captcha_3blHDdS.png')

#make image gray 
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

#Blur
blur = cv2.GaussianBlur(gray,(5,5),0)
bilateral = cv2.bilateralFilter(gray,5,75,75)

#Thresholding
ret, thresh = cv2.threshold(bilateral,25,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)

#Kernal
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))

#other things
erosion = cv2.erode(thresh,kernel,iterations = 1)
closing = cv2.morphologyEx(erosion, cv2.MORPH_CLOSE, kernel, iterations = 1)

#Transform image
dist_transform = cv2.distanceTransform(closing,cv2.DIST_L2,5)
ret, sure_fg = cv2.threshold(dist_transform,0.02*dist_transform.max(),255,cv2.THRESH_BINARY)#,255,0)

#kernel_1
kernel_1 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (1, 2))

dilation_1 = cv2.dilate(sure_fg,kernel_1,iterations = 2)
erosion_1 = cv2.erode(dilation_1,kernel_1,iterations = 3)

plt.imshow(erosion_1, 'gray')

Any help would be greatly appreciated, Here are more examples of the type of images that are produced from the captcha;

also heres the link to a folder containing the images

Answer 1

Here is a C# solution using OpenCvSharp (which should be easy to convert back to python/c++ because the method names are exactly the same).

It uses OpenCV'sinpainting technique to avoid destroying too much of the letters before possibly running an OCR phase. We can see that the lines have a different color than the rest, so we'll use that information very early, before any grayscaling/blackwhiting. Steps are as follow:

• build a mask from the lines using their color (#707070)
• dilate that mask a bit because the lines may have been drawn with antialiasing
• repaint ("inpaint") the original image using this mask, which will remove the lines while preserving most of what was below the lines (letters). Note we could remove the small points before that step, I think it would be even better
• apply some dilate/blur/threshold to finalize

Here is the mask:

Here is the result:

Here is the result on sample set:

Here is the C# code:

static void Decaptcha(string filePath)
{
    // load the file
    using (var src = new Mat(filePath))
    {
        using (var binaryMask = new Mat())
        {
            // lines color is different than text
            var linesColor = Scalar.FromRgb(0x70, 0x70, 0x70);

            // build a mask of lines
            Cv2.InRange(src, linesColor, linesColor, binaryMask);
            using (var masked = new Mat())
            {
                // build the corresponding image
                // dilate lines a bit because aliasing may have filtered borders too much during masking
                src.CopyTo(masked, binaryMask);
                int linesDilate = 3;
                using (var element = Cv2.GetStructuringElement(MorphShapes.Ellipse, new Size(linesDilate, linesDilate)))
                {
                    Cv2.Dilate(masked, masked, element);
                }

                // convert mask to grayscale
                Cv2.CvtColor(masked, masked, ColorConversionCodes.BGR2GRAY);
                using (var dst = src.EmptyClone())
                {
                    // repaint big lines
                    Cv2.Inpaint(src, masked, dst, 3, InpaintMethod.NS);

                    // destroy small lines
                    linesDilate = 2;
                    using (var element = Cv2.GetStructuringElement(MorphShapes.Ellipse, new Size(linesDilate, linesDilate)))
                    {
                        Cv2.Dilate(dst, dst, element);
                    }

                    Cv2.GaussianBlur(dst, dst, new Size(5, 5), 0);
                    using (var dst2 = dst.BilateralFilter(5, 75, 75))
                    {
                        // basically make it B&W
                        Cv2.CvtColor(dst2, dst2, ColorConversionCodes.BGR2GRAY);
                        Cv2.Threshold(dst2, dst2, 255, 255, ThresholdTypes.Otsu);

                        // save the file
                        dst2.SaveImage(Path.Combine(
                            Path.GetDirectoryName(filePath),
                            Path.GetFileNameWithoutExtension(filePath) + "_dst" + Path.GetExtension(filePath)));
                    }
                }
            }
        }
    }
}

Answer 2

Take a closer look to your captcha. most of the dust in that image has a different grayscale value than the text.

The text is in 140 and the dust is in 112 .

A simple grayscale filtering will help a lot here.

from scipy.misc import imread, imsave
import numpy as np

infile = "A1nO4.png"
outfile = "A1nO4_out.png"

im = imread(infile, True)
out_im = np.ones(im.shape) * 255

out_im[im == 140] = 0

imsave(outfile, out_im)

Now use cv2.dilate ( cv2.erode on a white on black text) to get rid of the remaining dust.

Answer 3

This is not a very robust solution but it might be help full in most of the cases:

By seeing the image sample posted above i can observe one common feature about the diagonal lines that they either start or end at the image edges while the text which we are interested in are in the middle so in this way we can determine the pixel values of those diagonal lines by searching them in the first and last few rows and columns of the image matrix and eliminate them as noise. And this approach also might be less time costly.