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Sign Language Recognition from Digital Videos Using Deep Learning Methods

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Geometry and Vision (ISGV 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1386))

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Abstract

In this paper, we investigate the state-of-the-art deep learning methods for sign language recognition. In order to achieve this goal, Capsule Network (CapsNet) is proposed in this paper, which shows positive result. We also propose a Selective Kernel Network (SKNet) with attention mechanism in order to extract spatial information. Sign language as an important means of communications, the problems of recognizing sign language from digital videos in real time have become the new challenge of this research field. The contributions of this paper are: (1) The CapsNet attains the accuracy of overall recognition up to 98.72% based on our own dataset. (2) SKNet with attention mechanism is able to achieve the best recognition accuracy 98.88%.

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Authors and Affiliations

  1. Auckland University of Technology, Auckland, 1010, New Zealand

    Jia Lu, Minh Nguyen & Wei Qi Yan

Authors
  1. Jia Lu
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  2. Minh Nguyen
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  3. Wei Qi Yan
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Corresponding authors

Correspondence to Jia Lu , Minh Nguyen or Wei Qi Yan .

Editor information

Editors and Affiliations

  1. Auckland University of Technology, Auckland, New Zealand

    Minh Nguyen

  2. Auckland University of Technology, Auckland, New Zealand

    Wei Qi Yan

  3. Auckland Bioengineering House, Auckland, New Zealand

    Harvey Ho

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Lu, J., Nguyen, M., Yan, W.Q. (2021). Sign Language Recognition from Digital Videos Using Deep Learning Methods. In: Nguyen, M., Yan, W.Q., Ho, H. (eds) Geometry and Vision. ISGV 2021. Communications in Computer and Information Science, vol 1386. Springer, Cham. https://doi.org/10.1007/978-3-030-72073-5_9

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  • DOI: https://doi.org/10.1007/978-3-030-72073-5_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72072-8

  • Online ISBN: 978-3-030-72073-5

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