South African Sign Language Recognition Using Feature Vectors and Hidden Markov Models
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Date
2010
Authors
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Journal ISSN
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Publisher
University of the Western Cape
Abstract
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%.
Description
>Magister Scientiae - MSc
Keywords
Hidden Markov models, Personal computer (PC), South African Sign Language (SASL), Recognition and Animation, University of the Western Cape (UWC), Gesture Recognition (GR)