Software-based Malaysian sign language recognition

Farrah Wong, and Ali Chekima, and Faysal Ezwen Jupirin, and Yona Falinie Abdul Gaus, and Sainarayanan Gopala, and Wan Mahani Abdullah, (2013) Software-based Malaysian sign language recognition. Springer, Berlin, Heidelberg, pp. 297-306.


Download (42kB) | Preview


This work presents the development of a software-based Malaysian Sign Language recognition system using Hidden Markov Model. Ninety different gestures are used and tested in this system. Skin segmentation based on YCbCr colour space is implemented in the sign gesture videos to separate the face and hands from the background. The feature vector of sign gesture is represented by chain code, distance between face and hands and tilting orientation of hands. This work has achieved recognition rate of 72.22%.

Item Type: Book
Uncontrolled Keywords: Hidden Markov Model, gestures, skin segmentation
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: SCHOOL > School of Engineering and Information Technology
Depositing User: Unnamed user with email
Date Deposited: 28 Nov 2016 05:16
Last Modified: 11 Oct 2017 06:52

Actions (login required)

View Item View Item

Browse Repository
   UMS News
Quick Search

   Latest Repository

Link to other Malaysia University Institutional Repository

Malaysia University Institutional Repository