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i used this code:

    horizontalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (horizontalsize, 1))
    horizontal = cv2.erode(horizontal, horizontalStructure, (-1, -1))
    horizontal = cv2.dilate(horizontal, horizontalStructure, (-1, -1))

to remove lines.

and some filters to delete the noises and bold the font:

 blur = cv2.GaussianBlur(img, (11, 11), 0)
 thresh = cv2.threshold(blur, 80, 255, cv2.THRESH_BINARY)[1]
 kernel = np.ones((2,1), np.uint8)
 dilation = cv2.erode(thresh, kernel, iterations=1)
 dilation = cv2.bitwise_not(dilation)

Despite the threshold and other methods, as you can see lots of noise remained

This is the result I want to reach:

Do you know an OpenCV filter that will help me achieve this result?

Shalev
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  • There are many methods in opencv for image thresholding. You haven't written which one you have used. Have you seen this [documentation](https://docs.opencv.org/master/d7/d4d/tutorial_py_thresholding.html) ? – Alex Sveshnikov Mar 18 '20 at 13:27
  • I think without domain specific knowledge about your image data, there is no out of the box filter that can be applied. I think you are basically looking for vertical and horizontal lines that you want to remove from the image. If the is the case there are some edge detection filters that can help you. Maybe you can filter the the dected edge by blob size and blob's aspect ratio? But again, that stronlgy depends on how your images are structured. I think you are trying to remove certain features from the images not noise. – Paraboloid87 Mar 18 '20 at 13:42

1 Answers1

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The following solution is not a perfect, and not generic solution, but I hope it's good enough for your needs.

For removing the line I suggest using cv2.connectedComponentsWithStats for finding clusters, and mask the wide or long clusters.

The solution uses the following stages:

  • Convert image to Grayscale.
  • Apply threshold and invert polarity.
    Use automatic thresholding by applying flag cv2.THRESH_OTSU.
  • Use "close" morphological operation to close small gaps.
  • Find connected components (clusters) with statistics.
  • Iterate the clusters, and delete clusters with large width and large height.
    Remove very small clusters - considered to be noise.
  • The top and left side is cleaned "manually".

Here is the code: 

import numpy as np
import cv2

img = cv2.imread('Heshbonit.jpg')  # Read input image

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # Convert to Grayscale.

ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)  # Convert to binary and invert polarity

# Use "close" morphological operation to close small gaps
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, np.array([1, 1]));
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, np.array([1, 1]).T);

nlabel,labels,stats,centroids = cv2.connectedComponentsWithStats(thresh, connectivity=8)

thresh_size = 100

# Delete all lines by filling wide and long lines with zeros.
# Delete very small clusters (assumes to be noise).
for i in range(1, nlabel):
    #
    if (stats[i, cv2.CC_STAT_WIDTH] > thresh_size) or (stats[i, cv2.CC_STAT_HEIGHT] > thresh_size):
        thresh[labels == i] = 0
    if stats[i, cv2.CC_STAT_AREA] < 4:
        thresh[labels == i] = 0

# Clean left and top margins "manually":
thresh[:, 0:30] = 0
thresh[0:10, :] = 0

# Inverse polarity
thresh = 255 - thresh

# Write result to file
cv2.imwrite('thresh.png', thresh)
Rotem
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