The Efficacy of the Eigenvector Approach to South African Sign Language Identification
| dc.contributor.advisor | Connan, James | |
| dc.contributor.author | Segers, Vaughn Mackman | |
| dc.date.accessioned | 2022-03-09T13:10:43Z | |
| dc.date.accessioned | 2024-10-30T14:00:58Z | |
| dc.date.available | 2022-03-09T13:10:43Z | |
| dc.date.available | 2024-10-30T14:00:58Z | |
| dc.date.issued | 2010 | |
| dc.description | Masters of Science | en_US |
| dc.description.abstract | 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 approach. 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. | en_US |
| dc.identifier.uri | https://hdl.handle.net/10566/16988 | |
| dc.language.iso | en | en_US |
| dc.publisher | University of the Western Cape | en_US |
| dc.rights.holder | University of the Western Cape | en_US |
| dc.subject | South African Sign Language Recognition and Animation (SASL) | en_US |
| dc.subject | Integration of Signed and Verbal Communication | en_US |
| dc.subject | Motivation | en_US |
| dc.subject | Human Computer interaction (HCI) | en_US |
| dc.subject | Movement Hold Model (MHM) | en_US |
| dc.subject | British Sign Language (BSR) | en_US |
| dc.title | The Efficacy of the Eigenvector Approach to South African Sign Language Identification | en_US |