Real-time Sign Language Recognition Using MobileNetV2 and Transfer Learning

TL;DR

This paper presents a real-time Indian Sign Language recognition system utilizing MobileNetV2 and transfer learning to improve communication accessibility for the hearing-impaired community in India.

Contribution

It introduces a novel application of MobileNetV2 with transfer learning for efficient real-time ISL recognition, addressing a gap in accessible technology for Indian Sign Language.

Findings

Achieved high accuracy in sign language recognition

Demonstrated real-time performance on mobile devices

Enhanced communication accessibility for hearing-impaired users

Abstract

The hearing-impaired community in India deserves the access to tools that help them communicate, however, there is limited known technology solutions that make use of Indian Sign Language (ISL) at present. Even though there are many ISL users, ISL cannot access social and education arenas because there is not yet an efficient technology to convert the ISL signal into speech or text. We initiated this initiative owing to the rising demand for products and technologies that are inclusive and help ISL, filling the gap of communication for the ones with hearing disability. Our goal is to build an reliable sign language recognition system with the help of Convolutional Neural Networks (CNN) to . By expanding communication access, we aspire toward better educational opportunities and a more inclusive society for hearing impaired people in India.