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|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
|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
|Description: ||Link to publisher's homepage at http://ieeexplore.ieee.org/|
|Appears in Collections:||Conference Papers|
Sazali Yaacob, Prof. Dr.
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