| dc.contributor.advisor | Connan, James | |
| dc.contributor.author | Naidoo, Nathan Lyle | |
| dc.date.accessioned | 2022-03-07T08:11:15Z | |
| dc.date.available | 2022-03-07T08:11:15Z | |
| dc.date.issued | 2010 | |
| dc.identifier.uri | http://hdl.handle.net/11394/8808 | |
| dc.description | >Magister Scientiae - MSc | en_US |
| dc.description.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%. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | University of the Western Cape | en_US |
| dc.subject | Hidden Markov models | en_US |
| dc.subject | Personal computer (PC) | en_US |
| dc.subject | South African Sign Language (SASL) | en_US |
| dc.subject | Recognition and Animation | en_US |
| dc.subject | University of the Western Cape (UWC) | en_US |
| dc.subject | Gesture Recognition (GR) | en_US |
| dc.title | South African Sign Language Recognition Using Feature Vectors and Hidden Markov Models | en_US |
| dc.rights.holder | University of the Western Cape | en_US |
