Remove uneven vertical and horizontal lines from an image using cv2 Python

Question

The objective is to remove uneven vertical and horizontal lines from an images using cv2 Python.

Currently, I am using these two code block to remove the horizontal and vertical lines.

Remove vertical and horizontal line

nimg_v=gray.copy()
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 40))
detLines = cv2.morphologyEx(nimg_v, cv2.MORPH_OPEN, kernel, iterations=2) #
nimg_v[(detLines !=0)]=0

# Remove horizontal lines
nimg_h=nimg_v.copy()
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (40,30))
detLines = cv2.morphologyEx(nimg_h, cv2.MORPH_OPEN, kernel, iterations=1)
nimg_h[(detLines !=0)]=0

Despite adjusting the Size of the structuring element, but I am still unable to remove most of the lines while maintaining the text.

The full code is

import cv2
import numpy as np
from matplotlib import pyplot as plt

dpath='so_images/dummy_image.jpg'

im = cv2.imread(dpath)
# Rough estimation the starting point of text region
y,x=50,700

# Rough estimation where the end of text region
y_end, x_end=1500,1350
white_bg = 255*np.ones_like(im)
white_bg[y:y+(y_end-y), x:x+(x_end-x)] =im[y:y+(y_end-y), x:x+(x_end-x)]
gray=cv2.cvtColor(white_bg, cv2.COLOR_BGR2GRAY)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
dilate = cv2.dilate(gray, kernel, iterations = 2)
idx = (dilate==255)
gray[idx]=0

## Remove vertical and horizontal line

nimg_v=gray.copy()
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 40))
detLines = cv2.morphologyEx(nimg_v, cv2.MORPH_OPEN, kernel, iterations=2) #
nimg_v[(detLines !=0)]=0

# Remove horizontal lines
nimg_h=nimg_v.copy()
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (40,30))
detLines = cv2.morphologyEx(nimg_h, cv2.MORPH_OPEN, kernel, iterations=1)
nimg_h[(detLines !=0)]=0



img_sm=nimg_h.copy()
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2,2))
dilate = cv2.dilate(img_sm, kernel, iterations = 4)

img_sm[(dilate !=0)]=255


img_cont=img_sm.copy()
schunk_small=800
schunk_big=50000
cnts = cv2.findContours(img_cont, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    area = cv2.contourArea(c)
    if (area < schunk_small) | (area>schunk_big):
        cv2.drawContours(img_cont, [c], -1, (0, 0, 0), -1)

plt.imshow(img_cont)
plt.show()

In addition, I also try using HoughLinesP and filter based on the slope <1 as below. However, I am still unable to remove the lines.

edges = cv2.Laplacian(img_cont,cv2.CV_8UC1) # Laplacian Edge Detection

lines = cv2.HoughLinesP(
    edges, # Input edge image
    1, # Distance resolution in pixels
    np.pi/180, # Angle resolution in radians
    threshold=100, # Min number of votes for valid line
    minLineLength=5, # Min allowed length of line
    maxLineGap=10 # Max allowed gap between line for joining them
)

lines_list = []

for points in lines:
    x1,y1,x2,y2=points[0]
    slope = ((y2-y1) / (x2-x1)) if (x2-x1) != 0 else np.inf
    if slope <= 1:
        cv2.line(img_cont,(x1,y1),(x2,y2),(255,255,255),1)

Answer 1

By convolving the image with low pass filter with a custom kernel can remove thin vertical and horizontal lines in your image. Since the thickness of alphanumeric characters is much higher than those lines, it can filter those lines. By adding below code gave me these results -

kernel = np.full(fill_value=1/(18*18*250), shape=(18,18), dtype=float)
img_rst = cv2.filter2D(img_cont,-1,kernel)
plt.imshow(img_rst)
plt.show()