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dc.contributor.authorRajkumar, Palaniappan
dc.contributor.authorPaulraj, Murugesa Pandiyan, Assoc. Prof. Dr.
dc.contributor.authorSazali, Yaacob, Prof. Dr.
dc.contributor.authorMohd Shuhanaz, Zanar Azalan
dc.date.accessioned2012-11-05T08:58:01Z
dc.date.available2012-11-05T08:58:01Z
dc.date.issued2010-10-16
dc.identifier.isbn978-967-5760-03-7
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/21620
dc.descriptionInternational Postgraduate Conference On Engineering (IPCE 2010), 16th - 17th October 2010 organized by Centre for Graduate Studies, Universiti Malaysia Perlis (UniMAP) at School of Mechatronic Engineering, Pauh Putra Campus, Perlis, Malaysia.en_US
dc.description.abstractSign language recognition is one of the most promising sub-fields in gesture recognition research. Effective sign language recognition would grant the deaf and hard-of-hearing expanded tools for communicating with both other people and machines. Hand gesture is one of the typical methods used in sign language for non-verbal communication. It is most commonly used by people who have hearing or speech problems to communicate among themselves or with normal people. 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 Affine Moment Blur invariants extracted from the right and left hand gesture images are used as feature vector to develop a network model. The system has been implemented and tested for its validity. Experimental results show that the recognition rate is 97.19%.en_US
dc.language.isoenen_US
dc.publisherUniversiti Malaysia Perlis (UniMAP)en_US
dc.relation.ispartofseriesProceedings of the International Postgraduate Conference on Engineering (IPCE 2010)en_US
dc.subjectSign language recognitionen_US
dc.subjectHand gestureen_US
dc.subjectAffine Moment Blur invariantsen_US
dc.titleA simple sign language recognition system using affine moment blur invariant featuresen_US
dc.typeWorking Paperen_US
dc.publisher.departmentCentre for Graduate Studiesen_US
dc.contributor.urlprkmect@gmail.comen_US


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