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Research on Dynamic Sign Language Recognition Based on Key Frame Weighted of DTW

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Multimedia Technology and Enhanced Learning (ICMTEL 2021)

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Abstract

Dynamic sign language can be described by its trajectory and key hand types. Most of the commonly used sign language can be recognized by trajectory curve matching. Therefore, In this paper, a new dynamic sign language recognition method is proposed, which uses trajectory and key hand type to extract features, adopts a key frame weighted DTW (dynamic time warping) algorithm to implement hierarchical matching strategy, and gradually matches sign language gestures from two levels of trajectory and key hand type, so as to effectively improve the accuracy and efficiency of sign language recognition.

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Acknowledgement

The work described in this paper was fully supported by a grant from the National Philosophy and Social Sciences Foundation of China (No.20BTQ065) and The Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 16KJB520026).

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Authors and Affiliations

  1. Nanjing Normal University of Special Education, Nanjing, 210038, China

    ShengWei Zhang, ZhaoSong Zhu & RongXin Zhu

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  1. ShengWei Zhang
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  2. ZhaoSong Zhu
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  3. RongXin Zhu
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Corresponding author

Correspondence to ZhaoSong Zhu .

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Editors and Affiliations

  1. Hunan Normal University, Changsha, China

    Weina Fu

  2. University of Jinan, Jinan, China

    Yuan Xu

  3. University of Leicester, Leicester, UK

    Shui-Hua Wang

  4. University of Leicester, Leicester, UK

    Yudong Zhang

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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Zhang, S., Zhu, Z., Zhu, R. (2021). Research on Dynamic Sign Language Recognition Based on Key Frame Weighted of DTW. In: Fu, W., Xu, Y., Wang, SH., Zhang, Y. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 388. Springer, Cham. https://doi.org/10.1007/978-3-030-82565-2_2

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  • DOI: https://doi.org/10.1007/978-3-030-82565-2_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-82564-5

  • Online ISBN: 978-3-030-82565-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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