AI-Based Sign Language Translator

• Author(s): Yogesh Balaji Reddy; Prathamesh Nanasaheb Raut; Aniket Sndipan Tandale; Prajesh Vikas Surwase; Prof. D. J. Waghmare
• Paper ID: 1712443
• Page: 2243-2249
• Published Date: 29-11-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 5 November-2025

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.

Keywords

Sign Language Translation, Artificial Intelligence, Deep Learning, CNN, Computer Vision.

Citations

IRE Journals:
Yogesh Balaji Reddy, Prathamesh Nanasaheb Raut, Aniket Sndipan Tandale, Prajesh Vikas Surwase, Prof. D. J. Waghmare "AI-Based Sign Language Translator" Iconic Research And Engineering Journals Volume 9 Issue 5 2025 Page 2243-2249 https://doi.org/10.64388/IREV9I5-1712443

IEEE:
Yogesh Balaji Reddy, Prathamesh Nanasaheb Raut, Aniket Sndipan Tandale, Prajesh Vikas Surwase, Prof. D. J. Waghmare "AI-Based Sign Language Translator" Iconic Research And Engineering Journals, 9(5) https://doi.org/10.64388/IREV9I5-1712443