An integrated sign language recognition system
| dc.contributor.advisor | Ghaziasgar, Mehrdad | |
| dc.contributor.advisor | Connan, James | |
| dc.contributor.author | Nel, Warren | |
| dc.date.accessioned | 2014-08-18T13:16:41Z | |
| dc.date.accessioned | 2024-10-30T14:00:47Z | |
| dc.date.available | 2014-08-18T13:16:41Z | |
| dc.date.available | 2024-10-30T14:00:47Z | |
| dc.date.issued | 2014 | |
| dc.description | Doctor Educationis | en_US |
| dc.description.abstract | Research has shown that five parameters are required to recognize any sign language gesture: hand shape, location, orientation and motion, as well as facial expressions. The South African Sign Language (SASL) research group at the University of the Western Cape has created systems to recognize Sign Language gestures using single parameters. Using a single parameter can cause ambiguities in the recognition of signs that are similarly signed resulting in a restriction of the possible vocabulary size. This research pioneers work at the group towards combining multiple parameters to achieve a larger recognition vocabulary set. The proposed methodology combines hand location and hand shape recognition into one combined recognition system. The system is shown to be able to recognize a very large vocabulary of 50 signs at a high average accuracy of 74.1%. This vocabulary size is much larger than existing SASL recognition systems, and achieves a higher accuracy than these systems in spite of the large vocabulary. It is also shown that the system is highly robust to variations in test subjects such as skin colour, gender and body dimension. Furthermore, the group pioneers research towards continuously recognizing signs from a video stream, whereas existing systems recognized a single sign at a time. To this end, a highly accurate continuous gesture segmentation strategy is proposed and shown to be able to accurately recognize sentences consisting of five isolated SASL gestures. | en_US |
| dc.identifier.uri | https://hdl.handle.net/10566/16956 | |
| dc.language.iso | en | en_US |
| dc.publisher | University of Western Cape | en_US |
| dc.rights.holder | University of Western Cape | en_US |
| dc.subject | Sign language | en_US |
| dc.subject | Sign language recognition | en_US |
| dc.subject | South African sign Language gesture recognition | en_US |
| dc.title | An integrated sign language recognition system | en_US |
| dc.type | Thesis | en_US |