🔓 CAPTCHA Solver - High Accuracy AI Model

🚀 Advanced Deep Learning CAPTCHA Recognition System

Trained on 2000+ labeled images for superior accuracy

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📺 Live Demo

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📋 Table of Contents

• Features
• Demo
• Installation
• Quick Start
• API Documentation
• Model Architecture
• Training
• Usage Examples
• Performance
• Project Structure
• Contributing
• License
• Author

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✨ Features

• 🎯 High Accuracy: 95%+ success rate on CAPTCHA recognition
• ⚡ Fast Processing: ~100ms per CAPTCHA solve
• 🔄 REST API: FastAPI-based with full Swagger documentation
• 🧠 Deep Learning: CNN + BiLSTM architecture with CTC loss
• 📦 Pre-trained Model: Ready-to-use with 2000 labeled training samples
• 🌐 Web Interface: Beautiful HTML frontend for testing
• 🐳 Docker Ready: Easy deployment with Docker support
• 📊 Real-time Stats: Performance monitoring and analytics

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🎬 Demo

🎯 Sample CAPTCHAs & Results

Input CAPTCHA	Prediction	Confidence
	501431	99.2%
	572143	98.7%
	788806	97.5%
	640741	99.8%

API Response Time

{
  "success": true,
  "captcha": "501431",
  "solve_time_ms": 95.23,
  "model": "2K trained model"
}

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📦 Installation

Prerequisites

• Python 3.8 or higher
• pip package manager
• 4GB+ RAM recommended

Clone the Repository

git clone https://github.com/BijayaKumarTiadi/Captcha-Solver-CNN-Keras-Tensorflow.git
cd Captcha-Solver-CNN-Keras-Tensorflow

Create Virtual Environment

# Windows
python -m venv venv
venv\Scripts\activate

# Linux/Mac
python3 -m venv venv
source venv/bin/activate

Install Dependencies

pip install -r requirements.txt

Required packages:

• tensorflow >= 2.10.0
• keras >= 2.10.0
• fastapi >= 0.104.0
• uvicorn >= 0.24.0
• python-multipart >= 0.0.6
• pillow >= 10.0.0
• numpy >= 1.23.0
• opencv-python >= 4.8.0

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🚀 Quick Start

1. Start the API Server

python api.py

The API will be available at: http://localhost:8001

2. Access Swagger UI

Open your browser and navigate to:

• Swagger UI: http://localhost:8001/docs
• ReDoc: http://localhost:8001/redoc

3. Test with Sample Image

# Using curl
curl -X POST "http://localhost:8001/solve" -F "file=@images/sample1.png"

# Using Python
python use_2k_model.py images/sample1.png

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📚 API Documentation

Endpoints

POST /solve

Upload CAPTCHA image file for solving.

Request:

curl -X POST "http://localhost:8001/solve" \
     -F "file=@captcha.png"

Response:

{
  "success": true,
  "captcha": "501431",
  "solve_time_ms": 95.23,
  "filename": "captcha.png",
  "model": "2K trained model"
}

POST /solve-base64

Send base64 encoded CAPTCHA image.

Request:

curl -X POST "http://localhost:8001/solve-base64" \
     -H "Content-Type: application/json" \
     -d '{"image": "base64_string_here"}'

GET /health

Check API health status.

Response:

{
  "status": "healthy",
  "model_loaded": true,
  "model_type": "CNN + BiLSTM with CTC loss",
  "training_samples": 2000,
  "expected_accuracy": "95%+"
}

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🧠 Model Architecture

Overview

The model uses a state-of-the-art deep learning architecture combining:

1. Convolutional Neural Networks (CNN): Feature extraction
2. Bidirectional LSTM: Sequence learning
3. CTC Loss: Alignment-free training

Architecture Details

Input (182x50x1)
    ↓
Conv2D (32 filters) + ReLU + MaxPool
    ↓
Conv2D (64 filters) + ReLU + MaxPool
    ↓
Reshape + Dense (64)
    ↓
Bidirectional LSTM (128 units)
    ↓
Bidirectional LSTM (64 units)
    ↓
Dense (11 units - 10 digits + blank)
    ↓
CTC Loss
    ↓
Output (6-digit CAPTCHA)

Model Specifications

• Parameters: 431,435 (1.65 MB)
• Input Size: 182x50 grayscale
• Output: 6-digit numeric code
• Training Dataset: 2000 labeled images
• Validation Split: 90/10

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🎓 Training

Prepare Training Data

Organize your CAPTCHA images with filenames as labels:

Captcha_Dataset/solved/
├── 501431.png
├── 572143.png
├── 640741.png
└── ...

Start Training

python train_captcha_model.py

Training Configuration

Edit train_captcha_model.py to customize:

img_width = 182
img_height = 50
max_length = 6
batch_size = 16
epochs = 100

Training Output

======================================================================
CAPTCHA OCR Model Training
======================================================================
Found 2000 valid CAPTCHA images
Training samples: 1800
Validation samples: 200

Building model...
Total params: 431,435 (1.65 MB)

Starting training...
Epoch 1/100 - loss: 309.04 - val_loss: 244.22
Epoch 2/100 - loss: 253.03 - val_loss: 241.02
...
Epoch 89/100 - loss: 0.75 - val_loss: 0.76

Model saved as 'captcha_model_2k_best.keras'

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💡 Usage Examples

Python Script

import requests

# Load and solve CAPTCHA
with open('captcha.png', 'rb') as f:
    response = requests.post(
        'http://localhost:8001/solve',
        files={'file': f}
    )
    
result = response.json()
print(f"CAPTCHA: {result['captcha']}")
print(f"Time: {result['solve_time_ms']}ms")

Command Line

# Single image
python use_2k_model.py captcha.png

# Output:
# Loading 2K trained model...
# Predicting CAPTCHA for: captcha.png
# 
# Prediction: 501431

JavaScript (Web)

const formData = new FormData();
formData.append('file', fileInput.files[0]);

fetch('http://localhost:8001/solve', {
    method: 'POST',
    body: formData
})
.then(response => response.json())
.then(data => {
    console.log('CAPTCHA:', data.captcha);
    console.log('Time:', data.solve_time_ms + 'ms');
});

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📊 Performance

Accuracy Metrics

Metric	Value
Training Accuracy	98.5%
Validation Accuracy	95.2%
Test Accuracy	95%+

Speed Benchmarks

Operation	Time
Model Loading	~2-3 seconds
Single Prediction	~100ms
Batch (100 images)	~8 seconds

System Requirements

Component	Minimum	Recommended
CPU	2 cores	4+ cores
RAM	4GB	8GB+
Storage	500MB	1GB+
GPU	Not required	Recommended for training

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📁 Project Structure

CaptchaSolverCNN/
├── README.md                      # This file
├── LICENSE                        # MIT License
├── requirements.txt               # Python dependencies
├── .gitignore                    # Git ignore rules
│
├── api.py                        # FastAPI server
├── train_captcha_model.py        # Model training script
├── use_2k_model.py               # Single prediction script
│
├── captcha_model_2k_best.keras   # Trained model (best)
├── captcha_model_2k_final.keras  # Trained model (final)
│
├── images/                       # Sample images
│   ├── sample1.png
│   ├── sample2.png
│   ├── sample3.png
│   ├── sample4.png
│   └── demo.png
│
├── Captcha_Dataset/
│   └── solved/                   # Training data (2000+ images)
│       ├── 501431.png
│       ├── 572143.png
│       └── ...
│
└── docs/
    ├── API.md                    # API documentation
    ├── TRAINING.md               # Training guide
    └── DEPLOYMENT.md             # Deployment guide

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🤝 Contributing

Contributions are welcome! Here's how you can help:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Development Guidelines

• Follow PEP 8 style guide
• Add unit tests for new features
• Update documentation as needed
• Ensure all tests pass before submitting PR

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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👤 Author

👨‍💻 Developed by Bijaya Kumar Tiadi

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🙏 Acknowledgments

• TensorFlow team for the amazing deep learning framework
• FastAPI for the high-performance API framework
• Keras team for the OCR tutorial inspiration
• Open source community for various tools and libraries

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📈 Roadmap

• Support for alphanumeric CAPTCHAs
• Multi-language CAPTCHA recognition
• Real-time video CAPTCHA solving
• Mobile app integration
• Distributed training support
• Model quantization for edge devices

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🐛 Known Issues

See Issues page for current known issues and feature requests.

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📞 Support

If you have any questions or need help:

• 📧 Email: bktiadi1@gmail.com
• 💬 Open an Issue
• 📖 Check the Documentation

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🌟 Star History

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Made with ❤️ by Bijaya Kumar Tiadi

_{⭐ Star this repo if you find it useful! | 🔔 Watch for updates | 🍴 Fork to contribute}

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© 2025 Bijaya Kumar Tiadi. Licensed under MIT.