ASL Spelling Detection Using Deep Learning

Understanding American Sign Language is a difficult task for anyone who wish to communicate with the deaf and dumb. It creates a gap between people. This paper introduces a finger spelling recognition system designed to help bridge that gap. Our system uses a camera and specialized CNN architecture to understand hand gestures that represent letters. This approach offers a few key benefits: it uses the familiar alphabet, can be learned quickly, and works for people with different levels of hearing loss or sign language experience. This technology has the potential to make communication much easier for many people, especially in situations where spoken words or traditional signs might not be the best option. It detects the ASL alphabets and constructs words and sentences, also known as Finger-Spelling