Name
Title | ||
|---|---|---|
Investigation of Sign Language Recognition Performance by Integration of Multiple Feature Elements and Classifiers. |
Abstract | ||
|---|---|---|
Sign languages are used by healthy individuals when communicating with those who are hearing or speech impaired as well by those with hearing or speech impediments. It is quite difficult to acquire sign language skills since there are vast number of sign language words and some signing motions are very complex. Several attempts at machine translation have been investigated for a limited number of sign language motions by using KINECT and a data glove, which is equipped with a strain gauge to monitor the angles at which fingers are bent, to detect hand motions and hand shapes. |
Year | Venue | Field |
|---|---|---|
2018 | HCI | Wired glove,Computer science,Machine translation,Speech recognition,Sign language,Ensemble learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
3 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tatsunori Ozawa | 1 | 1 | 1.11 |
| Yuna Okayasu | 2 | 0 | 0.34 |
| Maitai Dahlan | 3 | 0 | 0.34 |
| Hiromitsu Nishimura | 4 | 47 | 7.82 |
| Hiroshi Tanaka | 5 | 1 | 0.77 |