Here’s a concise review of what you’ll typically find when searching for "captcha solver python github":
This report surveys Python-based CAPTCHA solvers on GitHub: common approaches, representative projects, capabilities and limits, typical architectures, legal/ethical considerations, and practical recommendations for research or defensive testing. Date: March 24, 2026.
If you need CAPTCHA solving for legitimate purposes (e.g., testing your own site):
captcha-solver-python by ahmetfurkandemir (educational CNN examples).Avoid repositories that claim to solve reCAPTCHA v3 or advanced hCaptcha for free – they're likely scams or outdated.
Would you like specific code examples for using a paid CAPTCHA API?
Python libraries and GitHub repositories for solving CAPTCHAs generally fall into two categories: API wrappers for paid services (like 2Captcha or Anti-Captcha) and OCR/AI-based local solvers 1. Official API Wrappers (Paid Services)
These libraries connect your Python script to external services that solve CAPTCHAs using human workers or advanced AI. 2captcha-python : The official Python SDK for
. It supports nearly all types, including reCAPTCHA (v2/v3), hCaptcha, and FunCaptcha. anticaptcha-python : The official library for Anti-Captcha
. It provides a unified API to handle various challenges like Cloudflare Turnstile and Amazon WAF. solvecaptcha-python : A newer client for integrating with the SolverCaptcha 2. Local & Lightweight OCR Solvers
These tools attempt to solve specific text-based or image-based CAPTCHAs locally on your machine without external API costs. amazoncaptcha
: A highly specialized, lightweight library designed specifically to solve Amazon's image CAPTCHAs with high accuracy. simple-CAPTCHA-solver : A classic example of a Python solver using the PIL (Pillow)
library to segment letters and match them against a known alphabet. MathCaptchaSolver
: A tool specifically designed to solve math-based CAPTCHAs (e.g., "5 + 2 = ?") by extracting and calculating the expression. : A bypass tool that leverages the Google Cloud Vision API to recognize and extract text from images for automation. 3. AI & Deep Learning Approaches
For more complex, distorted text, researchers use machine learning models like TensorFlow or PyTorch.
To put together a paper on a Python-based CAPTCHA solver, you can leverage established methodologies from GitHub repositories and academic preprints. Most modern solvers fall into two categories: Machine Learning (ML) approaches for image recognition and API-based automation for complex challenges like reCAPTCHA. 1. Abstract
The paper investigates automated methods for solving visual CAPTCHAs using Python to identify security vulnerabilities. It explores both Deep Learning-based solutions (like Convolutional Neural Networks) and automation-driven API integrations to achieve high accuracy. 2. Methodologies
You can structure your paper by comparing these three primary technical approaches:
Code examples of solving captchas in Python using ... - GitHub
This report examines Python-based CAPTCHA solving solutions on GitHub, categorized by their technical approach: automated Optical Character Recognition (OCR) and API-based service wrappers. 1. API-Based Service Wrappers
Most reliable GitHub projects for solving complex CAPTCHAs (like reCAPTCHA or hCaptcha) act as Python wrappers for paid third-party human-solving services. These are preferred for high-accuracy production needs.
Solvecaptcha-python: A streamlined library used to integrate the SolverCaptcha API into Python applications. It allows developers to send CAPTCHA challenges to a remote server and receive the solved text or token back. captcha solver python github
Metabypass-python: Another integration tool for the Metabypass service, supporting multiple CAPTCHA types through specialized API calls.
2-Captcha-Solver: A popular repository providing a Python 3 program to automate the interaction with the 2Captcha service. 2. Local OCR & Image Processing Solutions
For simple, text-based image CAPTCHAs, developers often use local libraries to avoid API costs.
Pytesseract & Selenium: A common method involves using Selenium for web automation and pytesseract for OCR.
Process: Download the image, preprocess it with PIL (Pillow) to enhance contrast, and use OCR to extract text.
Simple Captcha Solver: This repository demonstrates a "masking" technique. It slides letter templates across a CAPTCHA image to find the highest pixel-match score for each character. 3. Comparative Implementation Overview Local OCR (Tesseract) API Wrappers (2Captcha, etc.) Complexity High (requires image processing) Low (straightforward API calls) Accuracy Low (struggles with noise/distortion) Very High (uses human or AI farms) Cost Pay-per-solve Best For Simple text-based images reCAPTCHA v2/v3, hCaptcha, FunCaptcha 4. Usage Considerations
Configuration: API-based libraries typically require an API_KEY and an instance creation (e.g., solver = Solvecaptcha('YOUR_API_KEY')).
Ethics & Policies: Automated CAPTCHA solving must comply with the Target's Terms of Service. Many platforms, including GitHub, use CAPTCHAs specifically to prevent unauthorized bot activity.
Finding a reliable Python CAPTCHA solver on GitHub typically involves choosing between two main strategies: using OCR-based libraries for simple text images or integrating third-party API services for complex challenges like reCAPTCHA. Popular GitHub Repositories and Libraries
Solvecaptcha-Python: A client library designed to integrate with CAPTCHA solving services via an API key.
Simple CAPTCHA Solver: An educational project that demonstrates how to solve basic image-based CAPTCHAs by comparing pixel differences.
Captcha-Text-Extraction: A repository focused on using OpenCV and contours to isolate characters for machine learning prediction. Common Approaches
OCR and Image Processing: For basic text-based CAPTCHAs, developers often combine Pytesseract (an OCR tool) with OpenCV to clean and threshold images before extraction.
API Integration: For advanced challenges (v2/v3, hCaptcha), tools like the 2Captcha Python library are preferred. They outsource the solving process to a service that returns a token for submission.
Browser Automation: Tools like Selenium are frequently used to handle the web interaction while these libraries process the underlying challenge. Implementation Example (OCR Approach) For a simple text-based CAPTCHA, a basic workflow involves: Read the image using OpenCV. Apply thresholding to convert it to black and white. Find contours to identify individual characters. Extract text using a library like Tesseract.
Note on Ethics: While using a CAPTCHA solver is not inherently illegal, using it to scrape data from sites that explicitly forbid it in their Terms of Service may lead to IP bans or legal issues. simple CAPTCHA solver in python - GitHub
🚀 The Two Main Approaches To solve CAPTCHAs in Python, developers generally choose between two distinct methods:
Third-Party API Services: High success rates for complex CAPTCHAs (reCAPTCHA, hCaptcha) by using paid solving services.
Local Machine Learning (OCR): Free and self-hosted, but usually only works on simple, distorted-text image CAPTCHAs. 🛠️ Method 1: Using API Services (Recommended)
This is the most reliable method for modern scraping and automation. You send the CAPTCHA data to a provider, and they return the solution. Step 1: Choose a GitHub Library Here’s a concise review of what you’ll typically
Many services have official Python SDKs on GitHub to make integration seamless:
2Captcha Python SDK: Official library for the 2Captcha service.
SolveCaptcha Python: Official client for automated bypasses.
Captcha-Tools: An all-in-one community module that supports CapMonster, 2Captcha, AntiCaptcha, and CapSolver APIs. Step 2: Implementation (Example using 2Captcha) Install the package: pip install 2captcha-python Use code with caution. Copied to clipboard Basic Python Script:
from twocaptcha import TwoCaptcha # Initialize the solver with your API key solver = TwoCaptcha('YOUR_API_KEY') try: # For a normal image CAPTCHA result = solver.normal('path/to/captcha.jpg') print("Solved text: " + result['code']) except Exception as e: print(f"Error occurred: e") Use code with caution. Copied to clipboard 🤖 Method 2: Local OCR for Simple Image CAPTCHAs
If the target website uses simple, classic image CAPTCHAs (standard text with light background noise), you can build a solver locally for free. Step 1: Find an Open Source Model
Look for specialized repositories on GitHub depending on your level of expertise:
Beginner: Use simple-captcha-solver which uses basic image thresholding and pixel matching.
Advanced: Use Solving-Captchas, which utilizes TensorFlow and Convolutional Neural Networks (CNN) to read letters. Step 2: Implementation (Example using Tesseract OCR) Install dependencies: pip install pillow pytesseract selenium Use code with caution. Copied to clipboard
(Note: You must also install the Tesseract OCR binary on your operating system). Python Automation Script:
from PIL import Image from io import BytesIO import pytesseract from selenium import webdriver driver = webdriver.Chrome() driver.get('https://example.com') # Find the CAPTCHA image element img_element = driver.find_element("id", "captcha_image_id") img_bytes = img_element.screenshot_as_png # Process the image and read the text img = Image.open(BytesIO(img_bytes)) # Convert to grayscale to reduce noise img = img.convert('L') captcha_text = pytesseract.image_to_string(img) print(f"OCR guessed: captcha_text.strip()") Use code with caution. Copied to clipboard 💡 Pro-Tips for Github CAPTCHA Solvers
Browser Stealth: When automating with Selenium, CAPTCHAs are often triggered simply because the site detects automation. Use the Bypassing CAPTCHAs with Selenium guide to implement "stealth" configurations and minimize triggers without even needing to solve them.
Handle Base64: Many sites do not serve the CAPTCHA as a raw image file but as a Base64 string directly in the HTML. You will need the base64 python library to decode it before passing it to your OCR or API solver.
If you tell me what specific type of CAPTCHA you are dealing with (like an image with text, checkbox, or puzzle), I can provide a more tailored code script or recommend the exact GitHub repository suited for it.
Building or using a CAPTCHA solver in Python involves two main paths: creating a custom OCR (Optical Character Recognition) model for simple text images or integrating with third-party API services for complex challenges like reCAPTCHA. 1. Custom OCR Solutions (For Simple Text CAPTCHAs)
GitHub repositories often focus on image processing to extract text from basic distorted images. Preprocessing : Libraries like
are used to read the image, apply thresholding to remove noise, and find contours around individual letters. Masking & Matching
: A simple approach involves moving a "mask" (a template of a known letter) across the image to find the highest pixel-match score. Machine Learning : Modern projects use TensorFlow
to train convolutional neural networks (CNNs) on datasets of labeled CAPTCHA images to predict text with high accuracy. 2. API-Based Solutions (For reCAPTCHA, hCaptcha, etc.)
For advanced CAPTCHAs that rely on human-like behavior or image grids, Python scripts typically interact with a solver's API. Solver Libraries : Repositories like solvecaptcha-python Use a paid API (2Captcha, Capsolver, Anti-Captcha) –
provide a wrapper to send image files or site keys to a remote service. Send the CAPTCHA image or site key via solver.send()
Wait/sleep while the service solves it (usually 10–30 seconds). Retrieve the solution code via solver.get_result() to submit with your form. 3. Automation & Stealth Techniques When using tools like
, solving the CAPTCHA is only half the battle; avoiding detection is equally important. Bright Data Selenium Stealth : Developers use the selenium-stealth
plugin to hide automation signatures that trigger CAPTCHAs in the first place. Residential Proxies : Services like Bright Data
are often used to rotate IP addresses, mimicking real user traffic to bypass rate-limiting. Bright Data Key GitHub Libraries to Explore Library/Tool OpenCV / Tesseract Image preprocessing and basic text extraction. 2Captcha-Python Official Python SDK for the Undetected-Chromedriver An optimized Selenium driver to avoid bot detection. Playwright
A modern alternative to Selenium for faster browser automation. sample code snippet for a specific CAPTCHA type or more information on training a custom model
Python has become the primary language for automating CAPTCHA solving due to its rich ecosystem of AI libraries and integration with GitHub-hosted tools. Whether you are handling legacy image puzzles or modern invisible challenges like reCAPTCHA v3 and Cloudflare Turnstile, GitHub provides both ready-to-use SDKs for commercial services and open-source OCR (Optical Character Recognition) frameworks. Top Python CAPTCHA Solvers on GitHub (2026)
The following repositories are currently leading the market in terms of performance, maintenance, and community support:
CapSolver Python SDK: Widely regarded as a top choice for speed and reliability, particularly for Cloudflare challenges and Amazon WAF.
SolveCaptcha SDK: A modern, lightweight library supporting async/await, designed for seamless integration with browser automation tools like Selenium and Playwright.
Anti-Captcha Official: A veteran service with a highly stable API that uses a hybrid of AI and human workers for 99.9% uptime.
Simple-CAPTCHA-Solver: An open-source project ideal for lightweight image-based CAPTCHAs, using PIL (Pillow) and basic pixel comparison for local processing.
Puzzle-Captcha-Solver: Specializes in solving slide-based puzzle CAPTCHAs for platforms like TikTok and Binance using OpenCV for image detection. How to Implement a CAPTCHA Solver in Python
Implementing these tools typically follows one of two paths: using a professional API service or building a local OCR pipeline.
1. Using an API-Based Solver (Recommended for reCAPTCHA/Cloudflare)
API services are the most reliable for modern challenges because they handle rotating browser fingerprints and behavioral scoring.
# Example using solvecaptcha-python SDK from solvecaptcha import Solvecaptcha # Initialize the solver with your API key solver = Solvecaptcha('YOUR_API_KEY') # Solve a reCAPTCHA v2 result = solver.recaptcha_v2( site_key='SITE_KEY_HERE', page_url='https://example.com' ) print(f"Solved Token: result['code']") Use code with caution. Installation: pip install solvecaptcha-python. 2. Local OCR Solver (For Simple Text/Images)
If you are dealing with basic, non-distorted images, you can use local libraries to avoid API costs.
Before diving into the code, it is important to distinguish between the two main approaches found on GitHub:
Here are the most noteworthy repositories as of 2025, ranked by stars, activity, and usefulness.