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Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/9889

Title: A phoneme based sign language recognition system using skin color segmentation
Authors: Paulraj, Murugesa Pandiyan, Prof. Madya
Sazali, Yaacob, Prof. Dr.
Mohd Shuhanaz, Zanar Azalan
Palaniappan, Rajkumar
Keywords: Sign language recognition;Hand gesture;Moment invariants;Neural network;Skin segmentation
Issue Date: 21-May-2010
Publisher: Institute of Electrical and Elctronics Engineering (IEEE)
Citation: p. 1-5
Series/Report no.: Proceedings of the 6th International Colloquium on Signal Processing and Its Applications (CSPA) 2010
Abstract: A sign language is a language which, instead of acoustically conveyed sound patterns, uses visually transmitted sign patterns. Sign languages are commonly developed for deaf communities, which can include interpreters, friends and families of deaf people as well as people who are deaf or hard of hearing themselves. Developing a sign language recognition system will help the hearing impaired to communicate more fluently with the normal people. This paper presents a simple sign language recognition system that has been developed using skin color segmentation and Artificial Neural Network. The moment invariants features extracted from the right and left hand gesture images are used to develop a network model. The system has been implemented and tested for its validity. Experimental results show that the average recognition rate is 92.85%.
Description: Link to publisher's homepage at http://ieeexplore.ieee.org/
URI: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5545253
ISBN: 978-1-4244-7121-8
Appears in Collections:Paulraj Murugesa Pandiyan, Prof. Dr.
Conference Papers
Sazali Yaacob, Prof. Dr.

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