Real-Time Sign Language Interpretation Using Deep Learning Models for Accessible Communication

This project presents a real-time American Sign Language (ASL) recognition system using a webcam-based interface and a lightweight Convolutional Neural Network (CNN) model, specifically designed for alphabet gesture classification. The system addresses communication challenges faced by hearing and speech-impaired individuals by enabling seamless gesture-totext conversion using deep learning. A custom ASL dataset was created using webcam input in a controlled environment to ensure diversity in hand shapes, positions, and backgrounds. To improve gesture segmentation accuracy, YCrCb color space was utilized for effective skin detection. The CNN model was trained to classify 26 ASL alphabet gestures with a remarkable accuracy of 96.3%. Real-time implementation was achieved using OpenCV and TensorFlow on low-cost computing hardware, ensuring accessibility and performance. The system demonstrates stability across varying lighting conditions and hand orientations. It offers potential integration with assistive technologies such as voice converters or mobile applications, thus promoting inclusivity and accessibility in daily communication. This work contributes a practical, cost-effective, and efficient ASL recognition solution adaptable for educational, social, and healthcare settings.