American Sign Language Detection System

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.

Technologies Used

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

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