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South African Sign Language Recognition Using Feature Vectors and Hidden Markov Models

>Magister Scientiae - MSc / This thesis presents a system for performing whole gesture recognition for South African Sign Language. The system uses feature vectors combined with Hidden Markov models. In order to construct a feature vector, dynamic segmentation must occur to extract the signer's hand movements. Techniques and methods for normalising variations that occur when recording a signer performing a gesture, are investigated. The system has a classification rate of 69%.

Identiferoai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uwc/oai:etd.uwc.ac.za:11394/8808
Date January 2010
CreatorsNaidoo, Nathan Lyle
ContributorsConnan, James
PublisherUniversity of the Western Cape
Source SetsSouth African National ETD Portal
LanguageEnglish
Detected LanguageEnglish
RightsUniversity of the Western Cape

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