使用Tesseract v5.0.0识别阿拉伯文本（突尼斯车牌）出现反转问题求助

Hey there! I’ve dealt with this exact reversed Arabic text issue in Tesseract before, so let me break down how to fix it for your Tunisian license plate use case.

The Root Cause #

Arabic is a right-to-left (RTL) language, and Tesseract’s default settings sometimes struggle to correctly interpret the text direction for short, single-line inputs like license plates. This leads to the reversed output you’re seeing.

Step-by-Step Fixes #

Here are the key adjustments to your code and setup:

• Add Image Preprocessing: Grayscale conversion and thresholding reduce noise and make characters more distinct for Tesseract, improving both accuracy and direction detection.
• Specify Page Segmentation Mode (PSM): For license plates (single line of text), use --psm 7 (treats the image as a single text line) or --psm 8 (single word). This tells Tesseract to focus on a narrow, linear text block instead of analyzing a full page layout.
• Verify Arabic Language Pack: Double-check that you installed the Arabic language pack when setting up Tesseract. If not, reinstall Tesseract and make sure to select the ara option during installation.

Modified Working Code #

import pytesseract
import cv2

# Set path to Tesseract executable
pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"

# Read and preprocess the image
img = cv2.imread('text.jpg')
# Convert to grayscale
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Apply thresholding to clean up the image
_, threshold_img = cv2.threshold(gray_img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

# Configure Tesseract for Arabic single-line text
custom_config = r'--psm 7 -l ara'
recognized_text = pytesseract.image_to_string(threshold_img, config=custom_config)

# If you still see reversed text (rare with the above config), uncomment the line below
# recognized_text = recognized_text[::-1]

print("Recognized Text:", recognized_text)

# Display preprocessed image for reference
cv2.imshow("Preprocessed Image", threshold_img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Testing Tips #

• If the thresholding looks too harsh, try adjusting the threshold value manually instead of using OTSU.
• For particularly blurry license plates, add a slight blur (like cv2.GaussianBlur) before thresholding to reduce noise further.

内容的提问来源于stack exchange，提问作者Ameni Neffati