Horizontal Line detection with OpenCV

Question

I am trying to find horizontal and vertical lines from an image which came from a "document". The documents are scanned pages from contracts and so the lines look like what you would see in a table or in a contract block.

I have been trying OpenCV for the job. The Hough transform implementation in OpenCV seemed useful for the job, but I could not find any combination of parameters that would allow it to cleanly find the vertical and horizontal lines. I tried with and without edge detection. No luck. If anyone has done anything similar I'm interested in knowing how.

See here an image of my before and after experimentation with HoughP in OpenCV. It's the best I could do, http://dl.dropbox.com/u/3787481/Untitled%201.png

So now I'm wondering whether there is another kind of transform I could use which would allow me to reliably find horizontal and vertical lines (and preferably dashed lines too).

I know this problem is solvable because I have Nuance and ABBYY OCR tools which can both reliably extract horizontal and vertical lines and return me the bounding box of the lines.

Thanks! Patrick.

Answer 1

Have you seen a code sample from HoughLinesP function documentation?

I think you can use it as starting point for your algorithm. To pick horizontal an vertical lines you just need to filter out other lines by line angle.

UPDATE:

As I see you need to find not the lines but horizontal an vertical edges on the page. For this task you need to combine several processing steps to get good results.

For your image I'm able to get good results by combining Canny edge detection with HoughLinesP. Here is my code (I've used python, but I think you see the idea):

img = cv2.imread("C:/temp/1.png") gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) edges = cv2.Canny(gray, 80, 120) lines = cv2.HoughLinesP(edges, 1, math.pi/2, 2, None, 30, 1); for line in lines[0]:     pt1 = (line[0],line[1])     pt2 = (line[2],line[3])     cv2.line(img, pt1, pt2, (0,0,255), 3) cv2.imwrite("C:/temp/2.png", img) 

Result looks like:

Answer 2

Here's a complete OpenCV solution using morphological operations.

• Obtain binary image
• Create horizontal kernel and detect horizontal lines
• Create vertical kernel and detect vertical lines

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Here's a visualization of the process. Using this input image:

Binary image

import cv2  # Load image, convert to grayscale, Otsu's threshold image = cv2.imread('1.png') result = image.copy() gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY) thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1] 

Detected horizontal lines highlighted in green

# Detect horizontal lines horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (40,1)) detect_horizontal = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, horizontal_kernel, iterations=2) cnts = cv2.findContours(detect_horizontal, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnts = cnts[0] if len(cnts) == 2 else cnts[1] for c in cnts:     cv2.drawContours(result, [c], -1, (36,255,12), 2) 

Detected vertical lines highlighted in green

# Detect vertical lines vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,10)) detect_vertical = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, vertical_kernel, iterations=2) cnts = cv2.findContours(detect_vertical, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnts = cnts[0] if len(cnts) == 2 else cnts[1] for c in cnts:     cv2.drawContours(result, [c], -1, (36,255,12), 2) 

Result

Here's the output using another input image

Input -> Binary -> Detected Horizontal -> Detected Vertical -> Result

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Note: Depending on the image, you may have to modify the kernel size. For instance to capture longer horizontal lines, it may be necessary to increase the horizontal kernel from (40, 1) to say (80, 1). If you wanted to detect thicker horizontal lines, then you could increase the width of the kernel to say (80, 2). In addition, you could increase the number of iterations when performing cv2.morphologyEx(). Similarly, you could modify the vertical kernels to detect more or less vertical lines. There is a trade-off when increasing or decreasing the kernel size as you may capture more or less of the lines. Again, it all varies depending on the input image

Full code for completeness

import cv2  # Load image, convert to grayscale, Otsu's threshold image = cv2.imread('1.png') result = image.copy() gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY) thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]  # Detect horizontal lines horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (40,1)) detect_horizontal = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, horizontal_kernel, iterations=2) cnts = cv2.findContours(detect_horizontal, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnts = cnts[0] if len(cnts) == 2 else cnts[1] for c in cnts:     cv2.drawContours(result, [c], -1, (36,255,12), 2)  # Detect vertical lines vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,10)) detect_vertical = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, vertical_kernel, iterations=2) cnts = cv2.findContours(detect_vertical, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnts = cnts[0] if len(cnts) == 2 else cnts[1] for c in cnts:     cv2.drawContours(result, [c], -1, (36,255,12), 2)  cv2.imshow('result', result) cv2.waitKey()