For simple, old-school CAPTCHAs, pytesseract combined with PIL (Pillow) and OpenCV for preprocessing (greyscale, thresholding, erosion) can achieve 80-90% accuracy.

This will fail on CAPTCHAs with curved lines, overlapping characters, or variable fonts. Method 2: API-Based Solver Using 2Captcha (Production Ready) For real-world applications, use an API client. Most GitHub repos mirror this pattern.

# Use Tesseract with configuration for single line of text custom_config = r'--oem 3 --psm 8 -c tessedit_char_whitelist=ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789' text = pytesseract.image_to_string(denoised, config=custom_config)

import cv2 import pytesseract from PIL import Image def solve_simple_captcha(image_path): # Load image with OpenCV img = cv2.imread(image_path)

# Remove noise with median blur denoised = cv2.medianBlur(thresh, 3)

# Apply threshold to get black and white image _, thresh = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY_INV)

In the modern landscape of web scraping, automated testing, and digital automation, CAPTCHAs remain one of the most persistent roadblocks. For Python developers, the quest to find a reliable, efficient, and cost-effective solution often leads to a single search query: "captcha solver python github" .