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    Interpreting Arabic Sign Alphabet by using the Deep Learning

    Al-Obaidi, A. and Al-Nima, R. and Han, Tingting (2022) Interpreting Arabic Sign Alphabet by using the Deep Learning. In: International Conference on Sustainable Development Techniques, 29-30 Jun 2022, Mosul City, Iraq. (In Press)

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    Abstract

    Sign Language (SL) is a communication method between people. It is an essential language; especially for people who are speech impaired and hearing impaired, it can be considered as their mother tongues. Hand gestures form the nonverbal communication of this language. We focus on interpreting Arabic Sign Alphabet (ASA) in this study and, as a case study, the recognition of alphabet in Iraqi Sign Language (IrSL) is carried out with the help of specialists from the “Al-Amal Institute for the Deaf and Dumb”. A new ASA dataset of various hand gestures was created and adopted. In addition, a deep learning model named the Deep Arabic Sign Alphabet (DASA) is proposed, which is a developed version of the Convolutional Neural Network (CNN). It can efficiently interpret the ASA, achieving a high interpretation accuracy of 95.25%.

    Metadata

    Item Type: Conference or Workshop Item (Paper)
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Tingting Han
    Date Deposited: 21 Mar 2023 13:55
    Last Modified: 09 Aug 2023 12:53
    URI: https://eprints.bbk.ac.uk/id/eprint/48306

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