Sign Language Recognition System
Overview
Developed a real-time vision-based system for American Sign Language (ASL) gesture recognition. The system uses optimized CNN architectures to provide low-latency inference for accessibility applications.
Key Features
- Real-time Processing: Low-latency inference for live gesture recognition
- High Accuracy: 95% classification accuracy on ASL gestures
- Optimized Architecture: Custom CNN design for efficient processing
- Accessibility Focus: Designed to bridge communication gaps
Technical Implementation
System Architecture
- Computer Vision Pipeline: Real-time video processing and gesture extraction
- CNN Models: Optimized convolutional neural networks for gesture classification
- Inference Optimization: Techniques to minimize latency for real-time performance
Technologies Used
- Computer Vision: OpenCV for video processing
- Deep Learning: CNNs for gesture classification
- Real-time Processing: Optimized inference pipeline
- Programming: Python for implementation
Performance Metrics
- Classification Accuracy: 95%
- Inference Speed: Real-time processing capability
- Gesture Coverage: Comprehensive ASL alphabet and common phrases
- Robustness: Consistent performance across different lighting conditions
Applications
- Accessibility Tools: Communication aid for deaf and hard-of-hearing individuals
- Educational Systems: ASL learning and practice applications
- Human-Computer Interaction: Gesture-based interface systems
Impact
This project contributes to accessibility technology by providing an accurate and efficient system for sign language recognition, potentially improving communication accessibility for the deaf and hard-of-hearing community.
Satyam Singh
Machine Learning Researcher | Neural Networks & Applied AI
Machine learning researcher focused on building efficient neural network architectures for real-world applications, spanning deep learning, computer vision, and NLP.