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)

Preprocessing to clean the image before performing text extraction can help. Here's a simple approach

• Convert image to grayscale and sharpen image
• Adaptive threshold
• Perform morpholgical operations to clean image
• Invert image

First we convert to grayscale then sharpen the image using a sharpening kernel

Next we adaptive threshold to obtain a binary image

Now we perform morphological transformations to smooth the image

Finally we invert the image

import cv2
import numpy as np

image = cv2.imread('1.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
sharpen_kernel = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])
sharpen = cv2.filter2D(gray, -1, sharpen_kernel)
thresh = cv2.threshold(sharpen, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
close = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=1)
result = 255 - close

cv2.imshow('sharpen', sharpen)
cv2.imshow('thresh', thresh)
cv2.imshow('close', close)
cv2.imshow('result', result)
cv2.waitKey()