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International Conference on Computers Helping People with Special Needs

ICCHP 2016: Computers Helping People with Special Needs pp 400–406Cite as

Classifiers in Arab Gloss Annotation System for Arabic Sign Language

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9759))

Abstract

As we know deaf people present about 70 million of the person in the word. 17 million of this community is only in Arabic word. Therefore this community of person require more and more attention from researchers and precisely SLMT (Sign Language Machine Translation) researchers to be able to practice their natural right which is communication with other person. In this context the research laboratory LaTICE of the University of Tunis lunched science many years the project WebSign [1] aiming to translate automatically a written text to sign language whatever the language as input (English, French, Arabic, etc.). WebSign is a Web application. It is based on the technology of avatar (animation in virtual world). The input of the system is a text in natural language. The output is a real-time and online interpretation in sign language. This interpretation is constructed thanks to a dictionary of word and signs. The creation of this dictionary can be made in an incremental way by users who propose signs corresponding to words [2]. Our work as a part of this project aims to develop a translation module from Arabic text to Sign Language to be integrated in the WebSign project. This module offers to Arab Deaf and hearing people a tool facilitating their communication. Anyone can use this tool to translate an Arabic written text to Arabic Sign Language (ArSL). In fact in this level, it’s very useful to define a transcription system for Arabic Sign Language based on Arabic Gloss. This intermediate annotation system is a textual representation of sign language that covers the different parameters of the sign with a simplified representation to avoid the complexity of understanding [3].

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Author information

Authors and Affiliations

  1. Research Laboratory LaTICE, Tunis National Higher School of Engineering (ENSIT), University of Tunis, 5, Avenue Taha Hussein, B.P. 56, Bab Mnara, 1008, Tunis, Tunisia

    Nadia Aouiti & Mohamed Jemni

Authors
  1. Nadia Aouiti
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  2. Mohamed Jemni
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Corresponding author

Correspondence to Nadia Aouiti .

Editor information

Editors and Affiliations

  1. Institute Integriert Studieren, Universität Linz, Linz, Austria

    Klaus Miesenberger

  2. Rehabilitationswissenschaften, Technische Universität Dortmund, Dortmund, Germany

    Christian Bühler

  3. Masaryk University , Brno, Czech Republic

    Petr Penaz

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© 2016 Springer International Publishing Switzerland

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Aouiti, N., Jemni, M. (2016). Classifiers in Arab Gloss Annotation System for Arabic Sign Language. In: Miesenberger, K., Bühler, C., Penaz, P. (eds) Computers Helping People with Special Needs. ICCHP 2016. Lecture Notes in Computer Science(), vol 9759. Springer, Cham. https://doi.org/10.1007/978-3-319-41267-2_56

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  • DOI: https://doi.org/10.1007/978-3-319-41267-2_56

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41266-5

  • Online ISBN: 978-3-319-41267-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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