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Leveraging Sign Language Processing with Formal SignWriting and Deep Learning Architectures

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Intelligent Systems (BRACIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14197))

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Abstract

Advances in sign language processing have not adequately kept pace with the tremendous progress that has been made in oral language processing. This fact serves as motivation for conducting research on the potential utilization of deep learning models within the domain of sign language processing. In this paper, we present a method that utilizes deep learning to build a latent and generalizable representation space for signs, leveraging Formal SignWriting notation and the concept of sentence-based representation to effectively address sign language tasks, such as sign classification. Extensive experiments demonstrate the potential of this method, achieving an average accuracy of \(81\%\) on a subset of 70 signs with only 889 training data and \(69\%\) on a subset of 338 signs with 3, 871 training data.

The authors of this work would like to thank the Center for Artificial Intelligence (C4AI-USP) and the support from the São Paulo Research Foundation (FAPESP grant #2019/07665-4) and from the IBM Corporation. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

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Notes

  1. 1.

    https://github.com/fernandoafreitas/bracis_2023_signwriting.

  2. 2.

    Translation to English: “The girl who fell from bike? ... She is in the hospital!”.

  3. 3.

    Mapping for English words: <GIRL FELL BIKE> ... <SHE THERE HOSPITAL>.

  4. 4.

    Position symbols were not found in the databases used in this paper.

  5. 5.

    https://www.signbank.org/.

  6. 6.

    Detailed information about the regular expression is available at https://datatracker.ietf.org/doc/html/draft-slevinski-signwriting-text-05#section-2.3.

  7. 7.

    Accessed on 2023-01-31.

  8. 8.

    The same sign can be represented in multiple ways, either due to slight variations in the execution of the gesture or by taking contextual factors into account during the signaling process.

  9. 9.

    https://www.tensorflow.org/.

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Authors and Affiliations

  1. School of Arts, Sciences and Humanities, University of São Paulo, São Paulo, Brazil

    Fernando de Almeida Freitas, Sarajane Marques Peres, Otávio de Paula Albuquerque & Marcelo Fantinato

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  1. Fernando de Almeida Freitas
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  2. Sarajane Marques Peres
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  3. Otávio de Paula Albuquerque
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  4. Marcelo Fantinato
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Correspondence to Fernando de Almeida Freitas .

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Editors and Affiliations

  1. Federal University of São Carlos, São Carlos, Brazil

    Murilo C. Naldi

  2. Centro Universitario da FEI, São Bernardo do Campo, Brazil

    Reinaldo A. C. Bianchi

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de Almeida Freitas, F., Peres, S.M., de Paula Albuquerque, O., Fantinato, M. (2023). Leveraging Sign Language Processing with Formal SignWriting and Deep Learning Architectures. In: Naldi, M.C., Bianchi, R.A.C. (eds) Intelligent Systems. BRACIS 2023. Lecture Notes in Computer Science(), vol 14197. Springer, Cham. https://doi.org/10.1007/978-3-031-45392-2_20

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  • DOI: https://doi.org/10.1007/978-3-031-45392-2_20

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