How to fill the gaps in letters after Canny edge d

问题:

I'm trying to do an Arabic OCR using Tesseract, but the OCR doesn't work unless the letters are filled with black color. How do I fill the gaps after Canny edge detection?

Here is a sample image and sample code:

import tesserocr
from PIL import Image
import pytesseract
import matplotlib as plt
import cv2
import imutils
import numpy as np

image = cv2.imread(r'c:\ahmed\test3.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

gray = cv2.bilateralFilter(gray,30,40,40)
#gray = cv2.GaussianBlur(gray,(1,1), 0)
gray =cv2.fastNlMeansDenoising(gray ,None, 4, 7, 21)

image = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
            cv2.THRESH_BINARY,11,2)
k = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 1))

blur = cv2.medianBlur(image,3)
erode = cv2.erode(blur, k)
dilat = cv2.dilate(erode,k)
cv2.imshow("gray", dilat)

#cv2.imshow("dilation", img_dilation)
#thresh = cv2.Canny(thresh, 70, 200)

#crop_img = gray[215:215+315, 783:783+684]
#cv2.imshow("cropped", crop_img)

#resize = imutils.resize(blur, width = 460)
#cv2.imshow("resize", resize)

text = pytesseract.image_to_string(dilat, lang='ara')
print(text)
with open(r"c:\ahmed\file.txt", "w", encoding="utf-8") as myfile:
    myfile.write(text)
cv2.waitKey(0)

Result:

This is a sample image that won't work with neither thresholding nor Canny.

回答1:

In this case, because the text is black, it is best to simply find all the black pixels.

One very simple way to accomplish this using NumPy is as follows:

import matplotlib.pyplot as pp
import numpy as np

image = pp.imread(r'/home/cris/tmp/Zuv3p.jpg')
bin = np.all(image<100, axis=2)

What this does is find all pixels where all three channels are below a value of 100. I picked the threshold of 100 sort of randomly, there probably are better ways to pick a threshold. :)

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Notes:

1- When working with color input, converting to gray-value image as first step is usually a bad idea. This throws away a lot of information. Sometimes it's appropriate, but in this case it is better not to.

2- Edge detection is really nice, but is usually the wrong approach. Use edge detection when you need to find edges. Use something else when you don't want just the edges.

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Edit: If for some reason np.all complains about the data type (it doesn't for me), you should be able to convert its input to the right type:

bin = np.all(np.array(image<100, dtype=np.bool), axis=2)

or maybe

bin = np.all(np.array(image<100, dtype=np.uint8), axis=2)