Abstract
In the recent past, research in the field of automatic sign language recognition using machine learning methods have demonstrated remarkable success and made momentous progression. This research article investigates the impact of machine learning in the state of the art literature on sign language recognition and classification. It highlights the issues faced by the present recognition system for which the research frontier on sign language recognition intends the solutions. In this article, around 240 different approaches have been compared that explore sign language recognition for recognizing multilingual signs. The research done by various authors is also studied, and some of the important research articles are also discussed in this article. Based on the inferences from these approaches, this article discussed how machine learning methods could benefit the field of automatic sign language recognition and the potential gaps that machine learning approaches need to address for the real-time sign language recognition.
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14 July 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-04314-w
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Acknowledgements
I wish to express my gratitude to Science & Engineering Research Board, Department of Science & Technology, Government of India for sanctioning the project under Start-up Research Grant program SRG/2019/001338 and for supporting the project ostensibly.
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Elakkiya, R. RETRACTED ARTICLE: Machine learning based sign language recognition: a review and its research frontier. J Ambient Intell Human Comput 12, 7205–7224 (2021). https://doi.org/10.1007/s12652-020-02396-y
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DOI: https://doi.org/10.1007/s12652-020-02396-y