I'm in the middle of developing a system that predict numbers from 7Seg LCD and I'm using for the matter tesseract OCR engine and it's wrapper for python pytesseract.
I'm taking pictures with a camera then cropping the Region of Interest and I found out that I have to enhance my Image quality to increase the accuracy of the OCR engine.
I used some Image processing techniques (gray scale --> Gaussian Blur --> threshold) and I got a quiet good image but tesseract still can't detect the numbers in the image.
I use the code:
image = cv2.imread('test.jpg')
image = image[50:200, 300:540]
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
image = cv2.GaussianBlur(image, (3,3), 0)
_, image = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
cv2.imshow('result', image)
cv2.waitKey()
cv2.destroyAllWindows()
cv2.imwrite('enhanced.jpg', image)
tess_dir_config = r'--tessdata-dir "C:\Program Files\Tesseract-OCR\tessdata"'
text = image_to_string(image, lang='letsgodigital', config=tess_dir_config)
print(text)
The Output Image:
The Input Image:
The engine usually have an empty output and if not it will not detect the number correctly.
Is there some sort of other image processing that I can use to get the potential of the Engine.
Note: I'am using letsgodigital weights