Sign Language Detection

Abstract

Abstract: Sign language is a crucial means of communication for individuals with hearing impairments, serving as a rich and expressive form of language. As the world becomes increasingly interconnected, there is a growing need for technology to facilitate effective communication between sign language users and the broader community. Sign language detection, a branch of computer vision and artificial intelligence, plays a pivotal role in bridging these communication gaps. This abstract provides an overview of the key aspects related to sign language detection. The primary focus is on the technological advancements and applications that contribute to the seamless integration of sign language into various aspects of daily life. The paper explores the challenges associated with recognizing and interpreting sign language gestures, considering factors such as variations in signing styles, lighting conditions, and diverse signing communities. The proposed paper delves into the methodologies employed in sign language detection systems, including image and video processing techniques, deep learning algorithms, and sensor technologies. It highlights the significance of dataset diversity and the role of machine learning in enhancing the accuracy and robustness of sign language detection models. Additionally, the paper discusses the potential integration of sign language detection into mainstream devices, applications, and assistive technologies to empower individuals with hearing impairments.