Redundancy removal for isolated gesture in Indian sign language and recognition using multi-class support vector machine

摘要

Sign language is a formal language used by the deaf and dumb people to communicate through bodily movement, especially of hands rather than speech. In this paper, we have presented a vision-based method for recognition of isolated sign considering static and dynamic behaviour of Indian sign language (ISL). The proposed methodology consists of three modules: preprocessing, feature extraction and classification. In the preprocessing module, various steps such as skin colour segmentation, redundant frames removal (RFR) algorithm and face elimination have been performed. The purpose of RFR algorithm is to remove redundant frames from the sign video to speed up the recognition task. In the feature extraction module, multiple features have been extracted. A multi-class support vector machine (MSVM) and Bayesian K-nearest neighbour (BKNN) are used to classify the signs. Experimentation with vocabulary of 21 sign from ISL is conducted and the results prove that the proposed method for recognition of gestured sign is effective and having high accuracy. Experimental results demonstrate that the proposed system can recognise signs with 95.3% accuracy.