Masters of Science / 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.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uwc/oai:etd.uwc.ac.za:11394/8867 |
| Date | January 2010 |
| Creators | Segers, Vaughn Mackman |
| Contributors | Connan, James |
| Publisher | University of the Western Cape |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Rights | University of the Western Cape |
Page generated in 0.0024 seconds