Year: 2025
Type: article
Abstract: Sign language is the primary communication method for deaf and hard-of-hearing (DHH) individuals, yet it remains unfamiliar to most non-DHH people, creating a significant communication gap. To address this, we propose an AI-powered Sign Language Translation system that utilizes computer vision and deep learning to interpret hand gestures in real time and convert them into readable text. Our system is based on a Convolutional Neural Network (CNN) model trained on American Sign Language (ASL) datasets and uses webcam input for gesture recognition. This paper outlines the design, methodology, and implementation of the system, discusses the challenges in sign language translation (SLT), and highlights possible improvements using transformer-based approaches.
Authors: Yogesh Balaji Reddy, Prathamesh Nanasaheb Raut, Aniket Sndipan Tandale, Prajesh Vikas Surwase, Prof. D. J. Waghmare
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7114904787
FWCI:
Cites:
Cited by:
Related to: