Skin Segmentation and SVM for Identification and Spotlighting of Hand Gesture for ISLR System

People who have hearing loss utilise sign language to communicate. It enables gestures and spoken language to communicate with one another by connecting letters, words, and phrases. The hearing-impaired community could benefit from a machine that can translate spoken language into sign language English, allowing them to interact with the general public. They will be helped to improve their skills and become more conscious of what they can do to advance humanity. In this study, we attempted to create an automated system that could identify sign language in challenging environments. It is possible to extract the part of the signer’s hand that matches their skin tone from a video of them signing. From the hand image, Extractive and categorical features that can identify the sign are used. Support vector machine is used for the categorization. India is diverse in terms of religion, culture, and language. There is no recognised sign language in India. There are numerous ISL dialects with verbal variations spoken in India. There are numerous varieties of Sign language, even in Kerala, a small state. In order to recognise the signs unique to our region, we are working to construct a SLR system.