User:Simon/self directed research/OCR preprocessing: Difference between revisions
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Pre-processing for OCR: | ==Pre-processing for OCR== | ||
This script applies transformations to the image before running OCR, resulting in a clearer result: | |||
<syntaxhighlight lang="python"> | |||
# 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") | 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") | 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, | 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) | 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) | |||
</syntaxhighlight> |
Latest revision as of 15:23, 20 June 2020
Pre-processing for OCR
This script applies transformations to the image before running OCR, resulting in a clearer result:
# 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)