<p>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 constuct a feature vector, dynamic segmentation must occur to extract the signer&rsquo / 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%</p>
Identifer | oai: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_8533_1297923615 |
Date | January 2010 |
Creators | Nathan Lyle Naidoo |
Source Sets | South African National ETD Portal |
Language | English |
Detected Language | English |
Type | Thesis and dissertation |
Format | |
Coverage | ZA |
Rights | Copyright: University of the Western Cape |
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