Removing horizontal underlines

Question

I am attempting to pull text from a few hundred JPGs that contain information on capital punishment records; the JPGs are hosted by the Texas Department of Criminal Justice (TDCJ). Below is an example snippet with personally identifiable information removed.

I've identified the underlines as being the impediment to proper OCR--if I go in, screenshot a sub-snippet and manually white-out lines, the resulting OCR through pytesseract is very good. But with underlines present, it's extremely poor.

How can I best remove these horizontal lines? What I have tried:

• Started on OpenCV doc's walkthrough: Extract horizontal and vertical lines by using morphological operations. Got stuck pretty quickly, because I know zero C++.
• Followed along with Removing Horizontal Lines in image - ended up with an illegible string.
• Followed along with Removing long horizontal/vertical lines from edge image using OpenCV - wasn't able to get the intuition behind sizing the array of zeros here.

Tagging this question with c++ in the hope that someone could help to translate Step 5 of the docs walkthrough to Python. I've tried a batch of transformations such as Hugh Line Transform, but I am feeling around in the dark within a library and area I have zero prior experience with.

import cv2  # Inverted grayscale img = cv2.imread('rsnippet.jpg', cv2.IMREAD_GRAYSCALE) img = cv2.bitwise_not(img)  # Transform inverted grayscale to binary th = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C,                             cv2.THRESH_BINARY, 15, -2)  # An alternative; Not sure if `th` or `th2` is optimal here th2 = cv2.threshold(img, 170, 255, cv2.THRESH_BINARY)[1]  # Create corresponding structure element for horizontal lines. # Start by cloning th/th2. horiz = th.copy() r, c = horiz.shape  # Lost after here - not understanding intuition behind sizing/partitioning 

Answer 1

All the answers so far seem to be using morphological operations. Here's something a bit different. This should give fairly good results if the lines are horizontal.

For this I use a part of your sample image shown below.

Load the image, convert it to gray scale and invert it.

import cv2 import numpy as np import matplotlib.pyplot as plt  im = cv2.imread('sample.jpg') gray = 255 - cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) 

Inverted gray-scale image:

If you scan a row in this inverted image, you'll see that its profile looks different depending on the presence or the absence of a line.

plt.figure(1) plt.plot(gray[18, :] > 16, 'g-') plt.axis([0, gray.shape[1], 0, 1.1]) plt.figure(2) plt.plot(gray[36, :] > 16, 'r-') plt.axis([0, gray.shape[1], 0, 1.1]) 

Profile in green is a row where there's no underline, red is for a row with underline. If you take the average of each profile, you'll see that red one has a higher average.

So, using this approach you can detect the underlines and remove them.

for row in range(gray.shape[0]):     avg = np.average(gray[row, :] > 16)     if avg > 0.9:         cv2.line(im, (0, row), (gray.shape[1]-1, row), (0, 0, 255))         cv2.line(gray, (0, row), (gray.shape[1]-1, row), (0, 0, 0), 1)  cv2.imshow("gray", 255 - gray) cv2.imshow("im", im) 

Here are the detected underlines in red, and the cleaned image.

tesseract output of the cleaned image:

Convthed as th( shot once in the she stepped fr< brother-in-lawii collect on life in applied for man to the scheme i| 

Reason for using part of the image should be clear by now. Since personally identifiable information have been removed in the original image, the threshold wouldn't have worked. But this should not be a problem when you apply it for processing. Sometimes you may have to adjust the thresholds (16, 0.9).

The result does not look very good with parts of the letters removed and some of the faint lines still remaining. Will update if I can improve it a bit more.

UPDATE:

Dis some improvements; cleanup and link the missing parts of the letters. I've commented the code, so I believe the process is clear. You can also check the resulting intermediate images to see how it works. Results are a bit better.

tesseract output of the cleaned image:

Convicted as th( shot once in the she stepped fr< brother-in-law. ‘ collect on life ix applied for man to the scheme i| 

tesseract output of the cleaned image:

)r-hire of 29-year-old . revolver in the garage ‘ red that the victim‘s h {2000 to kill her. mum 250.000. Before the kil If$| 50.000 each on bin to police. 

python code:

import cv2 import numpy as np import matplotlib.pyplot as plt  im = cv2.imread('sample2.jpg') gray = 255 - cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) # prepare a mask using Otsu threshold, then copy from original. this removes some noise __, bw = cv2.threshold(cv2.dilate(gray, None), 128, 255, cv2.THRESH_BINARY or cv2.THRESH_OTSU) gray = cv2.bitwise_and(gray, bw) # make copy of the low-noise underlined image grayu = gray.copy() imcpy = im.copy() # scan each row and remove lines for row in range(gray.shape[0]):     avg = np.average(gray[row, :] > 16)     if avg > 0.9:         cv2.line(im, (0, row), (gray.shape[1]-1, row), (0, 0, 255))         cv2.line(gray, (0, row), (gray.shape[1]-1, row), (0, 0, 0), 1)  cont = gray.copy() graycpy = gray.copy() # after contour processing, the residual will contain small contours residual = gray.copy() # find contours contours, hierarchy = cv2.findContours(cont, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE) for i in range(len(contours)):     # find the boundingbox of the contour     x, y, w, h = cv2.boundingRect(contours[i])     if 10 < h:         cv2.drawContours(im, contours, i, (0, 255, 0), -1)         # if boundingbox height is higher than threshold, remove the contour from residual image         cv2.drawContours(residual, contours, i, (0, 0, 0), -1)     else:         cv2.drawContours(im, contours, i, (255, 0, 0), -1)         # if boundingbox height is less than or equal to threshold, remove the contour gray image         cv2.drawContours(gray, contours, i, (0, 0, 0), -1)  # now the residual only contains small contours. open it to remove thin lines st = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)) residual = cv2.morphologyEx(residual, cv2.MORPH_OPEN, st, iterations=1) # prepare a mask for residual components __, residual = cv2.threshold(residual, 0, 255, cv2.THRESH_BINARY)  cv2.imshow("gray", gray) cv2.imshow("residual", residual)     # combine the residuals. we still need to link the residuals combined = cv2.bitwise_or(cv2.bitwise_and(graycpy, residual), gray) # link the residuals st = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (1, 7)) linked = cv2.morphologyEx(combined, cv2.MORPH_CLOSE, st, iterations=1) cv2.imshow("linked", linked) # prepare a msak from linked image __, mask = cv2.threshold(linked, 0, 255, cv2.THRESH_BINARY) # copy region from low-noise underlined image clean = 255 - cv2.bitwise_and(grayu, mask) cv2.imshow("clean", clean) cv2.imshow("im", im)