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The efficacy of the Eigenvector approach to South African sign language identification

<p>The communication barriers between deaf and hearing society mean that interaction between these communities is kept to a minimum. The South African Sign Language research group, Integration of Signed and Verbal Communication: South African Sign Language Recognition and Animation (SASL), at the University of the Western Cape aims to create technologies to bridge the communication gap. In this thesis we address the subject of whole hand gesture recognition. We demonstrate a method to identify South African Sign Language classifiers using an eigenvector ap- proach. The classifiers researched within this thesis are based on those outlined by the Thibologa Sign Language Institute for SASL. Gesture recognition is achieved in real- time. Utilising a pre-processing method for image registration we are able to increase the recognition rates for the eigenvector approach.</p>

Identiferoai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uwc/oai:UWC_ETD:http%3A%2F%2Fetd.uwc.ac.za%2Findex.php%3Fmodule%3Detd%26action%3Dviewtitle%26id%3Dgen8Srv25Nme4_2697_1298280657
Date January 2010
CreatorsVaughn Mackman Segers.
Source SetsSouth African National ETD Portal
LanguageEnglish
Detected LanguageEnglish
TypeThesis and dissertation
FormatPdf
CoverageZA
RightsCopyright: University of the Western Cape

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