A real-time American Sign Language (ASL) alphabet recognition system for assistive communication using TinyML

Sign language is a critical communication tool for individuals with hearing impairments. This study focuses on developing a hand gesture recognition system to identify the American Sign Language (ASL) static alphabet using a Convolutional Neural Network (CNN) model deployed on a TinyML kit. The system is designed for real-time classification, making it a practical and accessible assistive technology. The model was tested at different distances, achieving an accuracy of 93.62% at 17.5 cm and 89.47% at 23 cm. Future work includes extending the system to recognise dynamic gestures and conducting user evaluations to improve robustness and accuracy.