Project Overview
An advanced computer vision project that trains Convolutional Neural Networks to recognize American Sign Language gestures in real-time. The system processes video input to detect and classify ASL gestures, making communication more accessible. Built with deep learning frameworks and computer vision libraries for robust performance.
Key Features
- Real-time ASL gesture recognition from video input
- Custom CNN architecture for gesture classification
- Pre-trained model integration for improved accuracy
- Performance evaluation and metrics analysis
- Interactive Jupyter notebook for experimentation
- Accessibility-focused design principles
Technical Challenges
- Handling variations in lighting and background conditions
- Achieving high accuracy across diverse hand shapes and sizes
- Optimizing model performance for real-time processing
- Creating robust training datasets for gesture recognition