Deep learning shape trajectories for isolated word sign language recognition
Joint Authors
Fakhfakh, Sana
Bin Jumah, Yusra
Source
The International Arab Journal of Information Technology
Issue
Vol. 19, Issue 4 (31 Jul. 2022), pp.660-666, 7 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2022-07-31
Country of Publication
Jordan
No. of Pages
7
Main Subjects
Information Technology and Computer Science
Abstract EN
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%).
American Psychological Association (APA)
Fakhfakh, Sana& Bin Jumah, Yusra. 2022. Deep learning shape trajectories for isolated word sign language recognition. The International Arab Journal of Information Technology،Vol. 19, no. 4, pp.660-666.
https://search.emarefa.net/detail/BIM-1437337
Modern Language Association (MLA)
Fakhfakh, Sana& Bin Jumah, Yusra. Deep learning shape trajectories for isolated word sign language recognition. The International Arab Journal of Information Technology Vol. 19, no. 4 (Jul. 2022), pp.660-666.
https://search.emarefa.net/detail/BIM-1437337
American Medical Association (AMA)
Fakhfakh, Sana& Bin Jumah, Yusra. Deep learning shape trajectories for isolated word sign language recognition. The International Arab Journal of Information Technology. 2022. Vol. 19, no. 4, pp.660-666.
https://search.emarefa.net/detail/BIM-1437337
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 665-666
Record ID
BIM-1437337
