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localize_text_tesseract.py
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# USAGE
# python localize_text_tesseract.py --image apple_support.png
# python localize_text_tesseract.py --image apple_support.png --min-conf 50
# import the necessary packages
from pytesseract import Output
import pytesseract
import argparse
import cv2
pytesseract.pytesseract.tesseract_cmd = 'C:/Program Files/Tesseract-OCR/tesseract.exe'
# construct the argument parser 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("-c", "--min-conf", type=int, default=0,
help="mininum confidence value to filter weak text detection")
args = vars(ap.parse_args())
# load the input image, convert it from BGR to RGB channel ordering,
# and use Tesseract to localize each area of text in the input image
image = cv2.imread(args["image"])
rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
results = pytesseract.image_to_data(rgb, output_type=Output.DICT)
# loop over each of the individual text localizations
for i in range(0, len(results["text"])):
# extract the bounding box coordinates of the text region from
# the current result
x = results["left"][i]
y = results["top"][i]
w = results["width"][i]
h = results["height"][i]
# extract the OCR text itself along with the confidence of the
# text localization
text = results["text"][i]
conf = int(results["conf"][i])
# filter out weak confidence text localizations
if conf > args["min_conf"]:
# display the confidence and text to our terminal
print("Confidence: {}".format(conf))
print("Text: {}".format(text))
print("")
# strip out non-ASCII text so we can draw the text on the image
# using OpenCV, then draw a bounding box around the text along
# with the text itself
text = "".join([c if ord(c) < 128 else "" for c in text]).strip()
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.putText(image, text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX,
1.2, (0, 0, 255), 3)
# show the output image
cv2.imshow("Image", image)
cv2.waitKey(0)