Action Detection for Sign Language Using Machine Learning

Abstract

This research intends to build an effective and quick algorithm for identifying the alphabets in American Sign Language (ASL) using natural hand movements, increasing communication accessibility for people with hearing impaired limitations. The system's ultimate goal is to act as a translator between spoken language and sign language, enabling more effective and efficient communication between those with hearing loss and others who don't have any hearing loss. The research uses image processing, machine learning, and CNN-based artificial intelligence to recognize ASL movements and generate outputs that are simple to interpret. The potential impact of this work on communication accessibility for people who have hearing loss is significant.