Abstract
In this paper, we propose an efficient trajectories analysis solution for the recognition of Isolated Word Sign Language (IWSL). The key technique innovation in this work is the shape trajectories analysis based on the deep learning method and achieved impressive results on different IWSL data sets: German: Rheinisch Westfälische Technische Hochschule(RWTH): RWTH-Boston-50 and RWTH-Boston-104(95.83%), Signer-Independent Continuous Sign Language Recognition for Large Vocabulary Using Subunit Models (SIGNUM: 98.21%) and new Tunisian Sign Language database (TunSigns: 98%).
| Original language | English |
|---|---|
| Pages (from-to) | 660-666 |
| Number of pages | 7 |
| Journal | International Arab Journal of Information Technology |
| Volume | 19 |
| Issue number | 4 |
| DOIs | |
| State | Published - Jul 2022 |
| Externally published | Yes |
Keywords
- deep learning
- isolated word recognition
- RWTH-Boston dataset
- shape trajectory analysis
- Sign language
- SIGNUM corpora