Recognition of Indian Sign Language in Live Video
This work addresses communication barriers for the deaf and hard-of-hearing community in India by enabling natural interaction with bare hands, though it is incremental as it builds on existing computer vision techniques.
The paper tackled the problem of recognizing Indian Sign Language alphabets from live video by developing a system that processes continuous video sequences, achieving a success rate of 96.25%.
Sign Language Recognition has emerged as one of the important area of research in Computer Vision. The difficulty faced by the researchers is that the instances of signs vary with both motion and appearance. Thus, in this paper a novel approach for recognizing various alphabets of Indian Sign Language is proposed where continuous video sequences of the signs have been considered. The proposed system comprises of three stages: Preprocessing stage, Feature Extraction and Classification. Preprocessing stage includes skin filtering, histogram matching. Eigen values and Eigen Vectors were considered for feature extraction stage and finally Eigen value weighted Euclidean distance is used to recognize the sign. It deals with bare hands, thus allowing the user to interact with the system in natural way. We have considered 24 different alphabets in the video sequences and attained a success rate of 96.25%.