Name
Abstract | ||
|---|---|---|
This paper presents a sign language recognition method that uses gloves with colored regions and an optical camera. Hand and finger motions can be identified by the movement of the colored regions. The authors propose using six weak cues from each sign language motion, as determined by an HMM (Hidden Markov Model). Decoding and recognition is achieved by detecting characteristic combinations of cues. It was experimentally verified that an accurate recognition rate as high as 62.3% was achieved by looking for six cues per word while observing a list of 25 sign language words. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/PACRIM.2017.8121923 | 2017 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) |
Keywords | Field | DocType |
Sign Language Recognition,Color Gloves,Hidden Markov Model,Optical Camera,Feature Value | Colored,Performance enhancement,Computer science,Real-time computing,Speech recognition,Sign language,Decoding methods,Hidden Markov model | Conference |
ISSN | ISBN | Citations |
2154-5952 | 978-1-5386-0701-5 | 1 |
PageRank | References | Authors |
0.41 | 3 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuna Okayasu | 1 | 1 | 0.41 |
| Tatsunori Ozawa | 2 | 1 | 0.41 |
| Maitai Dahlan | 3 | 1 | 0.41 |
| Hiromitsu Nishimura | 4 | 5 | 2.61 |
| Hiroshi Tanaka | 5 | 56 | 13.71 |