CODEWITHSUNDEEP
pythonopencvprocessingocr
Brett C
Brett C

Reputation: 93

How to extract text from an image with a slight background present?

I'm looking to extract the text from an image, The output I am receiving is not very accurate. I wonder if there's any additional steps I can take to process the image more to increase the accuracy of this OCR.

I've looked into some of the different ways to process the image and improve the OCR results. The image is quite small and I've been able to blow it up slightly, but to no avail.

The image will always be horizontal, no other text will be present other than the numbers. The maximum number will go up to 55000.

An example of the image in question:

After image processing, my image is scaled up by 4 on the X and Y axis. And some saturation is removed, although this does not improve the accuracy at all.

image = self._process(scale=6, iterations=2)
text = pytesseract.image_to_string(image, config="--psm 7")

My process method is doing the following:

# Resize and desaturate.
image = cv2.resize(image, None, fx=scale, fy=scale, 
interpolation=cv2.INTER_CUBIC)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Apply dilation and erosion.
kernel = np.ones((1, 1), np.uint8)
image = cv2.dilate(image, kernel, iterations=iterations)
image = cv2.erode(image, kernel, iterations=iterations)

return image

Expected: "10411"

The actual value is varied, usually an unrecognizable string, or some numbers are parsed but the accuracy rate is too low to be usable.

Upvotes: 4

Views: 1417

Answers (1)

J.D.
J.D.

Reputation: 4561

I don't have experience with OCR, but I think you're on the right track: increasing the image size so the algorithm has more pixels to work with and increasing the distinction between the numbers and the background.

Tricks I added: thresholding the image, which creates a mask where only the white pixels remain. There were a few white blobs that were not numbers, so I used findContours to color those unwanted blobs black.

Result:

enter image description here

Code:

import numpy as np 
import cv2
# load image
image = cv2.imread('number.png')
# resize image
image = cv2.resize(image,None,fx=5, fy=5, interpolation = cv2.INTER_CUBIC)
# create grayscale
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# perform threshold
retr, mask = cv2.threshold(gray_image, 230, 255, cv2.THRESH_BINARY)
# find contours
ret, contours, hier = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# draw black over the contours smaller than 200 - remove unwanted blobs
for cnt in contours:
    # print contoursize to detemine threshold
    print(cv2.contourArea(cnt))
    if cv2.contourArea(cnt) < 200:
         cv2.drawContours(mask, [cnt], 0, (0), -1)

#show image
cv2.imshow("Result", mask)
cv2.imshow("Image", image)

cv2.waitKey(0)
cv2.destroyAllWindows()

Upvotes: 3

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