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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 1/2019

08.08.2017 | Original Article

A review of hand gesture and sign language recognition techniques

verfasst von: Ming Jin Cheok, Zaid Omar, Mohamed Hisham Jaward

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 1/2019

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Abstract

Hand gesture recognition serves as a key for overcoming many difficulties and providing convenience for human life. The ability of machines to understand human activities and their meaning can be utilized in a vast array of applications. One specific field of interest is sign language recognition. This paper provides a thorough review of state-of-the-art techniques used in recent hand gesture and sign language recognition research. The techniques reviewed are suitably categorized into different stages: data acquisition, pre-processing, segmentation, feature extraction and classification, where the various algorithms at each stage are elaborated and their merits compared. Further, we also discuss the challenges and limitations faced by gesture recognition research in general, as well as those exclusive to sign language recognition. Overall, it is hoped that the study may provide readers with a comprehensive introduction into the field of automated gesture and sign language recognition, and further facilitate future research efforts in this area.
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Metadaten
Titel
A review of hand gesture and sign language recognition techniques
verfasst von
Ming Jin Cheok
Zaid Omar
Mohamed Hisham Jaward
Publikationsdatum
08.08.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 1/2019
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-017-0705-5

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