A Comparative Analysis of Sign Language Detection System

One of the earliest and most natural forms of communication is sign language, but since most people do not know it and translators are hard to come by, we have developed a real-time technique for American fingerspelling based on neural networks. ASL stands for American Sign Language. Our approach involves running the hand through a filter first and then running it through a classifier that determines the class of the hand motions. For those with hearing loss, being able to recognize signs is essential to communicate. Sign language recognition (SLR) plays an important role in building communication barriers among the hearing-impaired community and the general population. Over the years, various SLR systems have been developed using different techniques and technologies, ranging from computer vision to machine learning. This research paper aims to provide a comprehensive comparative analysis of existing SLR systems, evaluating their performance, advantages, limitations, and potential applications. By examining different approaches and methodologies, this paper seeks to identify trends, challenges, and future directions in the field of sign language recognition.