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Extract panels from comic pages

How to extract the individual panels from a scanned page from a comic book.

Computer Vision

Today some classical computer vision with OpenCV. I needed a dataset of individual comic book panels for some model fine-tuning.

For example, take this page from an “Astérix” comic book:

The original page

After counting, we see, it contains 11 individual panels and we will extract those with a little list of OpenCV magic.

import cv2 as cv
import numpy as np

file = "page.jpg"
orignal_img = cv.imread(file)
height, width = orignal_img.shape[:-1]


First, convert it to grayscale. Color is just confusing-

img = cv.cvtColor(orignal_img, cv.COLOR_BGR2GRAY)
cv.imwrite("bw.jpg", img) 

After making it grayscale


To get rid of the fine features, apply some median blurring.

img = cv.medianBlur(img, 5)
cv.imwrite("blur.jpg", img) 

After blurring


Next, re-sharpen the panel edges. Use the basic classical sharpening kernel.

kernel = np.array([[0, -1, 0],
                   [-1, 5,-1],
                   [0, -1, 0]])
img = cv.filter2D(src=img, ddepth=-1, kernel=kernel)
cv.imwrite("sharpen.jpg", img) 

After sharpening


Time to go to binary. For this use Otsu’s method for thresholding. Looks already good, but we want to fille the white patterns in the panels as well as possible to make the contour detection easier.

_, img = cv.threshold(img, 0, 255, cv.THRESH_BINARY + cv.THRESH_OTSU)
cv.imwrite("otsu.jpg", img) 

After applying otsu thresholding


To paint over the little white features, apply a open up the binary mask. Now looks really black already!

kernel = np.ones((9,9), np.uint8)
img = cv.morphologyEx(img, cv.MORPH_OPEN, kernel)
cv.imwrite("open.jpg", img) 

After opening up


Finally, to fill the remaining white holes in the black panels, apply a flood-fill filter. As we see, except for two of the panels its returning perfect black rectangles. Having some remaining white holes is fine though, as we regularize the contour detection which smooth over it.

_, _, img, _ = cv.floodFill(img, np.zeros((height+2, width+2), np.uint8), (0,0), 255)
cv.imwrite("floodfill.jpg", img * 255) 

After flood-filling

Contour extraction

The cv.findContours method from OpenCV generally finds contours of objects in images. With the pre-processing we made sure that the only sensible objects remaining should be the panels. Nevertheless we filter the found countours by their size with a minimum area and a maximum area.

The contours found will not be perfect four-edge rectangles, therefore we apply cv.boundingRect which will fit a rectangle around the contour. This will almost be the same contour but we need the simple x,y, width, height rectangle to select from the original image.

contours, _ = cv.findContours(img, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
min_size = 0.01 # 1% minimum size
i = 0
for cnt in contours:
    if  min_size * img.size < cv.contourArea(cnt) < 0.5 * img.size:
        i += 1
        x,y,w,h = cv.boundingRect(cnt)
        panel = orignal_img[y:y+h, x:x+w]
        cv.imwrite(f"panel_{i:02}.jpg", panel) 

This will extract the panels looking like this:

Pretty good eh! And so easy!

Bonus: extract text

As a bonus, we extract the text from the saved panels with Python Tesseract. You might have noticed, that the given comic is in Russian. Therefore when installing Google’s Tesseract OCR you also have to install the extended language packs to also recognize Russian.

With Tesseract installed, getting the text from the images is easy:

texts = {}
for i in range(1, 12):
    # Do OCR with tesseract
    text = pytesseract.image_to_string("panel_{i:02}.jpg"), lang="rus", nice=1)
    # Remove new-lines, make lower-case and clean white-spaces
    text = text.replace("\n", " ").lower().strip()
    # Reduce to only word-characters
    text = re.sub('[^\w ]+','', text)
    # Reduce multiple spaces in the text to single space
    text = ' '.join(text.split())
    texts[i] = text

Sadly, the images are rather small and the text is not super-readable. Therefore for this page, Tesseract only recognizes the text in 5 out of the 11 panels:

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