A real-time computer vision system that recognizes handwritten digits using a webcam feed. The system uses a Convolutional Neural Network (CNN) trained on the MNIST dataset to provide instant digit recognition.

• Real-time digit recognition through webcam
• CNN model trained on MNIST dataset
• Live confidence scores for predictions
• User-friendly visualization
• Mixed precision training for better performance

RealTimeDigitRecognizer/
├── data_loader.py      # Handles MNIST dataset operations
├── model.py            # CNN model architecture
├── camera_utils.py     # Real-time video processing
├── main.py            # Main application entry
└── requirements.txt   # Project dependencies

• Python 3.10
• TensorFlow
• OpenCV
• NumPy
• scikit-learn

1. Clone the repository:

git clone https://github.com/[YOUR_USERNAME]/RealTimeDigitRecognizer.git
cd RealTimeDigitRecognizer

2. Install dependencies:

pip install -r requirements.txt

1. Run the main application:

python main.py

2. A window will open showing your webcam feed
3. Hold up a digit in the green rectangle
4. The system will display:
   • Recognized digit
   • Confidence score
5. Press 'q' to quit

The CNN model consists of:

• Input Layer (28x28x1)
• 2 Convolutional layers with MaxPooling
• Dense layer with dropout
• Output layer (10 classes)

• Training accuracy: >98% on MNIST
• Real-time inference
• Mixed precision training enabled

1. Fork the repository
2. Create your 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

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

• MNIST dataset for training data
• TensorFlow team for the deep learning framework
• OpenCV team for computer vision capabilities