Doctor Educationis / 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.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uwc/oai:etd.uwc.ac.za:11394/3584 |
| Date | January 2014 |
| Creators | Nel, Warren |
| Contributors | Ghaziasgar, Mehrdad, Connan, James |
| Publisher | University of Western Cape |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Rights | University of Western Cape |
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