CODEWITHSUNDEEP
pythonopencv
pinkWojak
pinkWojak

Reputation: 281

Is there a way to avoid OpenCV from removing red text during image preprocessing?

I have the following function to pre-process an image for Tesseract OCR, in most of the image the text is white, there can be green, red and purple text too. I want to be able to read all of that, but when I apply the thresholding during the pre-processing the red text is gone. Is there a way to avoid this? It doesn't happen with the green text unless it's dark green

def pre_process_img(img):
    open_cv_image = numpy.array(img)
    # Convert RGB to BGR
    open_cv_image = open_cv_image[:, :, ::-1].copy()

    img_gray = cv2.cvtColor(numpy.array(img), cv2.COLOR_BGR2GRAY)
    img_gray = cv2.resize(img_gray, None, fx=3, fy=3, interpolation=cv2.INTER_CUBIC)
    img_inverted = 255 - img_gray
    ret, thresh1 = cv2.threshold(img_inverted, 127, 255, cv2.THRESH_BINARY)
    # [DEBUG] show pre processed image
    # cv2.imshow("inverted", thresh1)
    # cv2.waitKey(0)
    return thresh1

In this function img is a PIL.Image.Image image, I convert it to an OpenCV image and apply preprocessing (turning into greyscale, rezising, inverting and binary thresholding). With psm 11 on Tesseract it has given a good enough result.

Btw If you have any suggestion to improve my pre_process_img function I'm open to listen. I'm new to OpenCV and I just stuck with the thing that gave me the best result from everything I've tried

This is my image here

Upvotes: 2

Views: 190

Answers (1)

fmw42
fmw42

Reputation: 53081

Convert from BGR to HSV colorspace in Python/OpenCV. Then simply threshold the value channel. Here is the value channel. You will see that all text is white (in this case).

enter image description here

Upvotes: 0

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