如何在 Python 中使用 OpenCV 和 Tesseract 处理信用卡字体

Question

I'm trying to read cards and output card numbers and expiry date with OpenCV.

import cv2
import pytesseract

filename = 'image1.png'
img = cv2.imread(filename)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
canny = cv2.Canny(gray, 50, 150, apertureSize=3)
result = pytesseract.image_to_string(canny)
print(f"OCR Results: {result}")

cv2.imshow('img', img)
cv2.imshow('canny', canny)

if cv2.waitKey(0) & 0xff == 27:
    cv2.destroyAllWindows()

1. Image before processing

2. Image after Canny

The result text does not look good. See the screenshot below:

Question: How can I properly handle the cards fonts well for better results. Any idea is highly appreciated.

Thanks.

Answer 1

It looks like the OCR is not working well when passing the edges of the text.
You better apply threshold instead of using Canny.

I suggest the following stages:

• Convert from BGR to HSV color space, and get the S (saturation) color channel of HSV.
  All gray pixels in S are zero, and colored pixels are above zero.
• Convert to binary using automatic threshold (use cv2.THRESH_OTSU ).
• Crop the contour with the maximum size.
  Because the image you posted contains some background.
• Apply OCR on the cropped area.

Here is the code:

import numpy as np
import cv2
import imutils  # https://pypi.org/project/imutils/
import pytesseract

pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'  # I am using Windows

img = cv2.imread('image1.png')  # Read input image

# Convert from BGR to HSV color space
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

# Get the saturation color channel - all gray pixels are zero, and colored pixels are above zero.
s = hsv[:, :, 1]

# Convert to binary using automatic threshold (use cv2.THRESH_OTSU)
ret, thresh = cv2.threshold(s, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

# Find contours (in inverted thresh)
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
cnts = imutils.grab_contours(cnts)

# Find the contour with the maximum area.
c = max(cnts, key=cv2.contourArea)

# Get bounding rectangle
x, y, w, h = cv2.boundingRect(c)

# Crop the bounding rectangle out of thresh
thresh_card = thresh[y:y+h, x:x+w].copy()

# OCR
result = pytesseract.image_to_string(thresh_card)
print(f"OCR Results:\n {result}")


# Show images for debugging
cv2.imshow('s', s)
cv2.imshow('thresh', thresh)
cv2.imshow('thresh_card', thresh_card)
cv2.waitKey(0)
cv2.destroyAllWindows()

---

OCR Result:

 Visa Classic

| By)

4000 1234 Sb18 9010

CARDHOLDER MARE
VISA

Still not perfect...

---

s:

thresh:

thresh_card: