The full text of this article


Redundancy removal for isolated gesture in Indian sign language and recognition using multi-class support vector machine
by Subhash Chand Agrawal; Anand Singh Jalal; Charul Bhatnagar
International Journal of Computational Vision and Robotics (IJCVR), Vol. 4, No. 1/2, 2014


Abstract: 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.

Online publication date: Tue, 18-Feb-2014


is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Vision and Robotics (IJCVR):
Login with your Inderscience username and password:


    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email