from PIL import Image
import pytesseract
import argparse
import cv2

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True, help="Path to the image")
args = vars(ap.parse_args())

# load the image and convert it to grayscale
image = cv2.imread(args["image"])
cv2.imshow("Original", image)

# Apply an "average" blur to the image

blurred = cv2.blur(image, (3,3))
cv2.imshow("Blurred_image", blurred)
img = Image.fromarray(blurred)
text = pytesseract.image_to_string(img, lang='eng')
print (text)
cv2.waitKey(0)

As as result i get = "Stay: in an Overwoter Bungalow $3»"

What about using Contour and taking unnecessary blobs from it ? might work

Tesseract, while being "good" is not the best. For critical applications I tend to use EasyOCR so maybe give that a shot.

It's free and opensource as well.

Here's an example:

import easyocr
reader = easyocr.Reader(['en']) # this needs to run only once to load the model into memory



result = reader.readtext('chinese.jpg')
print(result)