Year: 2025
Type: article
Abstract: : Communication is a vital aspect of human interaction, yet individuals with hearing and speech impairments often face significant barriers when engaging with the general population. The Indian Sign Language (ISL) serves as a primary mode of communication for such individuals, but a lack of understanding among non-signers limits effective interaction. This project aims to develop an Indian Sign Language Recognition System that bridges this communication gap by translating hand gestures into corresponding text or speech in real time. The system utilizes computer vision and machine learning techniques to detect and interpret hand movements accurately. Image acquisition is performed using a camera, followed by preprocessing, feature extraction, and classification using a trained deep learning model. The proposed system enhances accessibility and inclusivity by enabling seamless two-way communication between hearing-impaired and non-impaired individuals. This work contributes toward the development of a more interactive and socially integrated environment for all.
Authors: Pragati Bhagat, Shruti Bile, Nikita Kadam, B. B. Godbole
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: diamond
OpenAlex ID: W4416208713