Sign Language Analysis Using CNN Algorithm

Abstract

Sign language is a rich and diverse mode of communication used by Deaf and hard of-hearing communities around the world. This paper presents a comprehensive analysis of sign language, focusing on its linguistic and gestural elements. Our study encompasses an exploration of the structural properties of sign languages, their historical evolution, and the cognitive and neural mechanisms underlying sign language processing. We begin by providing an overview of sign language as a natural and fully developed language with its own syntax, semantics, and phonological features. Drawing from various sign languages, including American Sign Language (ASL), British Sign Language (BSL), and others, we investigate the grammatical aspects and iconicity within signs. Furthermore, we examine the role of facial expressions and non-manual markers in conveying essential linguistic information. This analysis also delves into the historical and cultural dimensions of sign languages, shedding light on the evolution of sign languages and the influence of Deaf culture on their development. It highlights the importance of recognizing sign languages as unique cultural and linguistic entities, distinct from spoken languages. Keywords:Computer Vision; Convolutional Neural Networks; Sign Language Recognition