User:Simon/self directed research/OCR preprocessing
Pre-processing for OCR:
# import the necessary packages
- from PIL
import Image
import pytesseract
import argparse
import cv2
import os
- construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
help="path to input image to be OCR'd")
ap.add_argument("-p", "--preprocess", type=str, default="thresh",
help="type of preprocessing to be done")
args = vars(ap.parse_args())
- load the example image and convert it to grayscale
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
- check to see if we should apply thresholding to preprocess the
- image
if args["preprocess"] == "thresh":
gray = cv2.threshold(gray, 0, 255,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
- make a check to see if median blurring should be done to remove
- noise
elif args["preprocess"] == "blur":
gray = cv2.medianBlur(gray, 3)
- write the grayscale image to disk as a temporary file so we can
- apply OCR to it
filename = "{}.png".format(os.getpid())
cv2.imwrite(filename, gray)
- load the image as a PIL/Pillow image, apply OCR, and then delete
- the temporary file
text = pytesseract.image_to_string(Image.open(filename))
os.remove(filename)
print(text)
- show the output images
cv2.imshow("Image", image)
cv2.imshow("Output", gray)
cv2.waitKey(0)