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A review of hand gesture and sign language recognition techniques

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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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Acknowledgements

This research was made possible by the funding of the Ministry of Higher Education Malaysia and Universiti Teknologi Malaysia through the Research University Tier 1 Grant (Vote No. 09H75).

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Authors and Affiliations

  1. Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Skudai, Malaysia

    Ming Jin Cheok & Zaid Omar

  2. School of Engineering, Monash University Malaysia, Subang Jaya, Malaysia

    Mohamed Hisham Jaward

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  1. Ming Jin Cheok
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  2. Zaid Omar
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  3. Mohamed Hisham Jaward
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Correspondence to Ming Jin Cheok.

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Funding

This study was funded by Ministry of Higher Education Malaysia and Universiti Teknologi Malaysia through the Research University Tier 1 Grant (Vote No. 09H75).

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The authors declare that they have no conflict of interest.

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Cheok, M.J., Omar, Z. & Jaward, M.H. A review of hand gesture and sign language recognition techniques. Int. J. Mach. Learn. & Cyber. 10, 131–153 (2019). https://doi.org/10.1007/s13042-017-0705-5

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