Question: I'm using the code below for segmenting the articles from an image of newspaper. def segmenter(image_received): # Process 1: Lines Detection img = image_received gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # convert to binary gray image edges = cv2.Canny(gray, 75, 150) # determine contours lines = cv2.HoughLinesP(edges, 0.017, np.pi / 180, 60, minLineLength=100, maxLineGap=0.1) # houghlines generation # drawing houghlines for line in lines: x1, y1, x2, y2 = line[0] cv2.line(img, (x1, y1), (x2, y2), (0, 0, 128), 12) # the houghlines of color (0,0,128) is drawn # Drawing brown border bold = cv2.copyMakeBorder( img, # image source 5, # top width 5, # bottomm width 5, # left width 5, # right width cv2.BORDER_CONSTANT, value=(0, 0, 128) # brown color value ) image = bold gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1] horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 1)) detected_lines = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, horizontal_kernel, iterations=2) cnts = cv2.findContours(detected_lines, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) cnts = cnts[0] if len(cnts) == 2 else cnts[1] for c in cnts: if int(len(c) >= 10): cv2.drawContours(image, [c], 0, (0, 17, 255), 1) vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 1)) detected_lines = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, vertical_kernel, iterations=2) cnts = cv2.findContours(detected_lines, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) cnts = cnts[0] if len(cnts) == 2 else cnts[1] for c in cnts: if int(len(c) >= 10): cv2.drawContours(image, [c], 0, (0, 17, 255), 1) cv2.imwrite(f'tmp/{str(str(uuid.uuid4()))}.jpg', image)  for instance the input image is  and the output image is :  There are three problems:  the output rectangles aren't complete in all cases. Images also are segmented inside articles as part of articles. But what I need is to segment only the text of the newspaper and crop all the other things out. Something like this one:  Consider the following image:  The article indicated by borders is not rectangular and is much more complicated. How can I achieve the correct borders using python open-cv or other image processing libraries?  (the question has an answer here for matlab. But I need a python code.

Answer: here my pipeline. I think can be optimized. Initialization %matplotlib inline import numpy as np import cv2 from matplotlib import pyplot as plt  Load image image_file_name = 'paper.jpg' image = cv2.imread(image_file_name) # gray convertion gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)  The first important thing is to remove the lines. So I search the lines. grad_x = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=3) grad_y = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=3) abs_grad_x = cv2.convertScaleAbs(grad_x) abs_grad_y = cv2.convertScaleAbs(grad_y) # threshold thresh_x = cv2.threshold(abs_grad_x, 0, 255, cv2.THRESH_OTSU)[1] thresh_y = cv2.threshold(abs_grad_y, 0, 255, cv2.THRESH_OTSU)[1] # bluring kernel_size = 3 blur_thresh_x = cv2.GaussianBlur(thresh_x,(kernel_size, kernel_size),0) blur_thresh_y = cv2.GaussianBlur(thresh_y,(kernel_size, kernel_size),0) # Run Hough on edge detected image rho = 1 # distance resolution in pixels of the Hough grid theta = np.pi / 180 # angular resolution in radians of the Hough grid threshold = 15 # minimum number of votes (intersections in Hough grid cell) min_line_length = 200 # minimum number of pixels making up a line max_line_gap = 1 # maximum gap in pixels between connectable line segments line_image = np.copy(gray) * 0 # creating a blank to draw lines on # Vertical lines vertical_lines = cv2.HoughLinesP(blur_thresh_x, rho, theta, threshold, np.array([]), min_line_length, max_line_gap) if vertical_lines is not None: for line in vertical_lines: for x1,y1,x2,y2 in line: # here it's possible to add a selection of only vertical lines if np.abs(y1-y2)> 0.1 * np.abs(x1-x2): cv2.line(line_image,(x1,y1),(x2,y2),255,5) # Horizontal lines horizontal_lines = cv2.HoughLinesP(blur_thresh_y, rho, theta, threshold, np.array([]), min_line_length, max_line_gap) if horizontal_lines is not None: for line in horizontal_lines: for x1,y1,x2,y2 in line: # here it's possible to add a selection of only horizontal lines if np.abs(x1-x2)> 0.1 * np.abs(y1-y2): cv2.line(line_image,(x1,y1),(x2,y2),255,5)   After I remove the lines from the threshold # threshold thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1] # remove lines clean_thresh = cv2.subtract(thresh, line_image)   Then I search the phrases # search the phrases dilatation_type = cv2.MORPH_RECT horizontal_dilatation = 20 #This is the gap. 20 for the first image, 10 for the second image vertical_dilatation = 1 element = cv2.getStructuringElement(dilatation_type, (2*horizontal_dilatation + 1, 2*vertical_dilatation+1), (horizontal_dilatation, vertical_dilatation)) dilatation_thresh = cv2.dilate(clean_thresh, element) # Fill filled_tresh = dilatation_thresh.copy() contours, hierarchy = cv2.findContours(dilatation_thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) for cnt in contours: cv2.drawContours(filled_tresh, [cnt], -1, 255, cv2.FILLED)   Now I detect the bounding boxes # Draw bounding boxes bounding_box1 = filled_tresh.copy() contours, hierarchy = cv2.findContours(bounding_box1, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) for cnt in contours: x,y,w,h = cv2.boundingRect(cnt) cv2.rectangle(bounding_box1,(x,y),(x+w,y+h),255,cv2.FILLED)   # REPEAT Draw bounding boxes and Find the mean text width mean_bb_width = 0 # mean bounding box width bounding_box2 = bounding_box1.copy() contours, hierarchy = cv2.findContours(bounding_box2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) num_cnt=0 for cnt in contours: x,y,w,h = cv2.boundingRect(cnt) cv2.rectangle(bounding_box2,(x,y),(x+w,y+h),255,cv2.FILLED) mean_bb_width = mean_bb_width+w num_cnt=num_cnt+1 mean_bb_width=mean_bb_width/num_cnt   Now I separate the titles from the text # define title what has width bigger than 1.5* mean_width min_title_width = 1.5 * mean_bb_width raw_title = np.copy(gray) * 0 raw_text = np.copy(gray) * 0 # separate titles from phrases contours, hierarchy = cv2.findContours(bounding_box2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) for cnt in contours: x,y,w,h = cv2.boundingRect(cnt) if w >=min_title_width : cv2.drawContours(raw_title, [cnt], -1, 255, cv2.FILLED) else : cv2.drawContours(raw_text, [cnt], -1, 255, cv2.FILLED)   and then the final processing image_out = image.copy() # Closing parameters horizontal_closing = 1 vertical_closing = 20 kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(horizontal_closing,vertical_closing)) # Processing titles # Closing closing_title = cv2.morphologyEx(raw_title, cv2.MORPH_CLOSE, kernel) # Find contours contours, hierarchy = cv2.findContours(closing_title, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # Draw bounding boxes bounding_title = closing_title.copy() for cnt in contours: x,y,w,h = cv2.boundingRect(cnt) cv2.rectangle(image_out,(x,y),(x+w,y+h),(255,0,0),2) # Processing text # Closing closing_text = cv2.morphologyEx(raw_text, cv2.MORPH_CLOSE, kernel) # Find contours contours, hierarchy = cv2.findContours(closing_text , cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # Draw bounding boxes bounding_text = closing_text.copy() for cnt in contours: x,y,w,h = cv2.boundingRect(cnt) cv2.rectangle(image_out,(x,y),(x+w,y+h),(0,255,0),2)  The result is  Changing the parameter horizontal_dilatation from 20 to 10, I obtain for the second image (where I remove the red border that you added) the following result