South African Sign Language Hand Shape and Orientation Recognition on Mobile Devices Using Deep Learning
| dc.contributor.advisor | Ghaziasgar, Mehrdad | |
| dc.contributor.advisor | Venter, Isabella | |
| dc.contributor.advisor | Dodds, Reginald | |
| dc.contributor.author | Jacobs, Kurt | |
| dc.date.accessioned | 2017-10-17T15:56:22Z | |
| dc.date.accessioned | 2024-10-30T14:00:56Z | |
| dc.date.available | 2017-10-17T15:56:22Z | |
| dc.date.available | 2024-10-30T14:00:56Z | |
| dc.date.issued | 2017 | |
| dc.description | >Magister Scientiae - MSc | en_US |
| dc.description.abstract | In order to classify South African Sign Language as a signed gesture, five fundamental parameters need to be considered. These five parameters to be considered are: hand shape, hand orientation, hand motion, hand location and facial expressions. The research in this thesis will utilise Deep Learning techniques, specifically Convolutional Neural Networks, to recognise hand shapes in various hand orientations. The research will focus on two of the five fundamental parameters, i.e., recognising six South African Sign Language hand shapes for each of five different hand orientations. These hand shape and orientation combinations will be recognised by means of a video stream captured on a mobile device. The efficacy of Convolutional Neural Network for gesture recognition will be judged with respect to its classification accuracy and classification speed in both a desktop and embedded context. The research methodology employed to carry out the research was Design Science Research. Design Science Research refers to a set of analytical techniques and perspectives for performing research in the field of Information Systems and Computer Science. Design Science Research necessitates the design of an artefact and the analysis thereof in order to better understand its behaviour in the context of Information Systems or Computer Science. | en_US |
| dc.description.sponsorship | National Research Foundation (NRF) | en_US |
| dc.identifier.uri | https://hdl.handle.net/10566/16983 | |
| 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 | en_US |
| dc.subject | Deep Learning techniques | en_US |
| dc.subject | Hand shape recognition | en_US |
| dc.title | South African Sign Language Hand Shape and Orientation Recognition on Mobile Devices Using Deep Learning | en_US |