Indian Sign Language Recognition Using Eigen Value Weighted Euclidean Distance Based Classification Technique

TL;DR

This paper proposes a Sign Language Recognition system for Indian Sign Language using an Eigen value weighted Euclidean distance classifier, achieving a recognition rate of 97% on 24 signs.

Contribution

It introduces a novel classification technique based on Eigen value weighted Euclidean distance for Indian Sign Language recognition.

Findings

Recognition accuracy of 97% on 24 signs

System effective with 240 sample images

Four-step process: Skin filtering, Hand cropping, Feature extraction, Classification

Abstract

Sign Language Recognition is one of the most growing fields of research today. Many new techniques have been developed recently in these fields. Here in this paper, we have proposed a system using Eigen value weighted Euclidean distance as a classification technique for recognition of various Sign Languages of India. The system comprises of four parts: Skin Filtering, Hand Cropping, Feature Extraction and Classification. Twenty four signs were considered in this paper, each having ten samples, thus a total of two hundred forty images was considered for which recognition rate obtained was 97 percent.