Name
Abstract | ||
|---|---|---|
We propose a Random-Forest based sign language identification system. The system uses low-level visual features and is based on the hypothesis that sign languages have varying distributions of phonemes (hand-shapes, locations and movements). We evaluated the system on two sign languages - British SL and Greek SL, both taken from a publicly available corpus, called Dicta Sign Corpus. Achieved average F1 scores are about 95% - indicating that sign languages can be identified with high accuracy using only low-level visual features. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICIP.2013.6738541 | Image Processing |
Keywords | Field | DocType |
sign language recognition,Random-Forest based sign language identification system,hand-shapes,low-level visual features,phonemes,Sign language,language identification,sign language identification | Computer science,Manually coded language,Identification system,Cued speech,Speech recognition,Sign language,Natural language processing,Artificial intelligence | Conference |
ISSN | Citations | PageRank |
1522-4880 | 4 | 0.46 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Binyam Gebrekidan Gebre | 1 | 48 | 6.12 |
| Peter Wittenburg | 2 | 25 | 3.29 |
| Tom Heskes | 3 | 1519 | 198.44 |