Malayalam Sign Language Identification using Finetuned YOLOv8 and Computer Vision Techniques

TL;DR

This paper presents a novel approach for Malayalam sign language recognition using fine-tuned YOLOv8 and computer vision, addressing data scarcity and aiming to assist hearing-impaired Malayalam speakers.

Contribution

It introduces a new dataset for Malayalam sign language and applies advanced deep learning techniques, specifically YOLOv8, for sign language identification.

Findings

Achieved comparable accuracy to existing sign language systems

Developed a labeled dataset for Malayalam sign language

Provided a baseline model for future research

Abstract

Technological advancements and innovations are advancing our daily life in all the ways possible but there is a larger section of society who are deprived of accessing the benefits due to their physical inabilities. To reap the real benefits and make it accessible to society, these talented and gifted people should also use such innovations without any hurdles. Many applications developed these days address these challenges, but localized communities and other constrained linguistic groups may find it difficult to use them. Malayalam, a Dravidian language spoken in the Indian state of Kerala is one of the twenty-two scheduled languages in India. Recent years have witnessed a surge in the development of systems and tools in Malayalam, addressing the needs of Kerala, but many of them are not empathetically designed to cater to the needs of hearing-impaired people. One of the major challenges is the limited or no availability of sign language data for the Malayalam language and sufficient efforts are not made in this direction. In this connection, this paper proposes an approach for sign language identification for the Malayalam language using advanced deep learning and computer vision techniques. We start by developing a labeled dataset for Malayalam letters and for the identification we use advanced deep learning techniques such as YOLOv8 and computer vision. Experimental results show that the identification accuracy is comparable to other sign language identification systems and other researchers in sign language identification can use the model as a baseline to develop advanced models.