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

The

Which produce

But, notice, there are still vertical and horizontal lines in the image. Most of these lines are not a perfect straight.

I intend to clear the all the lines and having an output, ideally something as below.

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)

Remark:

Since this is for OCR application, I am particularly interested to remove the vertical and horizontal lines below the text BJ. This is because, the OCR system unable to recognize the text if there is horizontal lines below the BJ

The Google Colab notebook to run the cleaning can be found from this

CodePudding user response：

Don't take screenshot from monitor. Because, you had 4 rectangle, you will see the edge of black border become white. there is no way I can go further. I cannot get rid of black power cable. So here is code:

import cv2 import numpy as np dpath='im1.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, (13,13)) dilate = cv2.dilate(gray, kernel, iterations = 1) idx = (dilate==255) gray[idx]=0 ## Remove vertical and horizontal line nimg_v=gray.copy() kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (11, 11)) detLines = cv2.morphologyEx(nimg_v, cv2.MORPH_OPEN, kernel, iterations=1) nimg_v[(detLines !=0)]=0 # Remove horizontal lines nimg_h=nimg_v.copy() kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (11,11)) detLines = cv2.morphologyEx(nimg_h, cv2.MORPH_OPEN, kernel, iterations=1) nimg_h[(detLines !=0)]=255 img_sm=nimg_h.copy() kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (4,4)) dilate = cv2.dilate(img_sm, kernel, iterations = 1) 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) #cv2.imwrite('Pic.jpg', img_cont) cv2.imshow('Pic', img_cont) cv2.waitKey(0)

I'm not using matplotlib. You can change it.

Output: