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Machine learning methods for sign language recognition: a critical review and analysis.

Adeyanju, I.A.; Bello, O.O.; Adegboye, M.A.


I.A. Adeyanju

O.O. Bello

M.A. Adegboye


Sign language is an essential tool to bridge the communication gap between normal and hearing-impaired people. However, the diversity of over 7000 present-day sign languages with variability in motion position, hand shape, and position of body parts making automatic sign language recognition (ASLR) a complex system. In order to overcome such complexity, researchers are investigating better ways of developing ASLR systems to seek intelligent solutions and have demonstrated remarkable success. This paper aims to analyse the research published on intelligent systems in sign language recognition over the past two decades. A total of 649 publications related to decision support and intelligent systems on sign language recognition (SLR) are extracted from the Scopus database and analysed. The extracted publications are analysed using bibliometric VOSViewer software to (1) obtain the publications temporal and regional distributions, (2) create the cooperation networks between affiliations and authors and identify productive institutions in this context. Moreover, reviews of techniques for vision-based sign language recognition are presented. Various features extraction and classification techniques used in SLR to achieve good results are discussed. The literature review presented in this paper shows the importance of incorporating intelligent solutions into the sign language recognition systems and reveals that perfect intelligent systems for sign language recognition are still an open problem. Overall, it is expected that this study will facilitate knowledge accumulation and creation of intelligent-based SLR and provide readers, researchers, and practitioners a roadmap to guide future direction.


ADEYANJU, I.A., BELLO, O.O. and ADEGBOYE, M.A. 2021. Machine learning methods for sign language recognition: a critical review and analysis. Intelligent systems with applications [online], 12, article 200056. Available from:

Journal Article Type Article
Acceptance Date Nov 16, 2021
Online Publication Date Dec 8, 2021
Publication Date Nov 30, 2021
Deposit Date Dec 10, 2021
Publicly Available Date Dec 10, 2021
Journal Intelligent systems with applications
Electronic ISSN 2667-3053
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 12
Article Number 200056
Keywords Artificial intelligence; Computer vision; Bibliometric analysis; Deaf-dumb hearing impaired; Intelligent systems; Scopus database; Sign language recognition; VOSviewer
Public URL


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