Seeking Assistance with Free Captcha Solver Script in Python

[Rostya]

I've been working on a script to solve simple image captchas using Python, aiming for a free and open-source solution. The script uses OCR (Optical Character Recognition) to read the captcha text, but I'm encountering accuracy issues. Here's a snippet of my current code:

import cv2
from pytesseract import image_to_string

def solve_captcha(image_path):
    # Load the image
    captcha_image = cv2.imread(image_path)
    
    # Preprocess the image for better OCR accuracy
    # This might include steps like converting to grayscale, thresholding, etc.
    # ...

    # Use pytesseract to convert image to text
    captcha_text = image_to_string(captcha_image)
    return captcha_text

# Test the function
captcha_solution = solve_captcha('path_to_captcha_image.jpg')
print("Captcha text:", captcha_solution)

The script is not consistently accurate in solving captchas. Can anyone suggest improvements or alternative methods to increase the accuracy of captcha solving using Python? Any guidance or additional code snippets would be greatly appreciated.

Thank you!

 

[Madi]

Your approach using OCR with pytesseract is a good start for solving simple captchas. Here are some suggestions to improve the accuracy of your captcha solver:

1. Image Preprocessing: The key to improving OCR accuracy is effective image preprocessing. This can include noise reduction, binarization, and segmentation. For instance, you can use adaptive thresholding to handle different lighting conditions in images.
2. Use of Contours: Sometimes, it's helpful to detect and isolate characters using contours before applying OCR.
3. Custom OCR Training: If you have a dataset of similar captchas, you could consider training a custom OCR model tailored to recognize the specific font and style used in those captchas.

Here's an improved version of your code with additional image preprocessing steps:

import cv2
from pytesseract import image_to_string

def preprocess_image(captcha_image):
    # Convert to grayscale
    gray = cv2.cvtColor(captcha_image, cv2.COLOR_BGR2GRAY)

    # Apply thresholding
    _, thresh = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY_INV)

    # Other preprocessing steps can be added here
    # ...

    return thresh

def solve_captcha(image_path):
    captcha_image = cv2.imread(image_path)
    processed_image = preprocess_image(captcha_image)
    captcha_text = image_to_string(processed_image)
    return captcha_text

# Test the function
captcha_solution = solve_captcha('path_to_captcha_image.jpg')
print("Captcha text:", captcha_solution)

In this revised version, the image is first converted to grayscale and then thresholding is applied to make the text stand out more clearly, which should improve the OCR results.

Remember, the effectiveness of these methods can vary based on the type of captcha you're dealing with. Experimenting with different preprocessing techniques is often key to finding the best solution.

Hope this helps improve your captcha solver!