Real time sign language recognition using depth sensor
by Jayesh Gangrade; Jyoti Bharti
International Journal of Computational Vision and Robotics (IJCVR), Vol. 9, No. 4, 2019

Abstract: Communication via gestures is a visual dialect utilised by deaf and hard-of-hearing (HoH) people group. This paper proposed a system for sign language recognition utilising human skeleton data provided from Microsoft's Kinect sensor to recognising sign gestures. The Kinect sensor generates the skeleton of a human body and distinguishes 20 joints in it. The proposed method utilises 11 out of 20 joints and extracts 35 novel features per frame, based on distances, angles and velocity involving upper body joints. Multi-class support vector machine classified the 35 Indian sign gestures in real time with accuracy of 87.6%. The proposed method is robust in cluttered environment and viewpoint variation.

Online publication date: Mon, 12-Aug-2019

The full text of this article 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 subs@inderscience.com