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    Sign Language Translation System Using Convolutional Neural Networks Approach

    Sign Language Translation System Using Convolutional Neural Networks Approach


    Sign language is one of the ways to communicate by not making sound, as it is communicated by using hands and expressions. Sign language does not get much attention from the public because it is used by the minority of the society, hence the limited resources available for learning them. This paper proposes a sign translation system called ReadMe that uses deep learning approach, specifically the Convolutional Neural Networks (CNNs) to train the recognition model. However, training ReadMe using the CNNs revealed a low accuracy of 39.8% due to small size of training dataset, hence testing is aggravating. In order to increase the recognition accuracy in the future, CNNs algorithm in ReadMe will be trained using a larger dataset from both American Sign Language (ASL) and Malaysian Sign Language (BIM). In addition, the system is also hoped to enable users to train new gestures. Only by means of crowdsourcing that the system able to expand its vocabulary without facing the knowledge engineering bottleneck.

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    Titel :

    Sign Language Translation System Using Convolutional Neural Networks Approach

    Weitere Titelangaben:

    Lect.Notes Mechanical Engineering

    Beteiligte:
    Zakaria, Muhammad Aizzat (Herausgeber:in) / Abdul Majeed, Anwar P. P. (Herausgeber:in) / Hassan, Mohd Hasnun Arif (Herausgeber:in) / Kasinathan, Vinothini (Autor:in) / Mustapha, Aida (Autor:in) / Hew, Hui Shan (Autor:in) / Hamed, Vazeerudeen Abdul (Autor:in)
    Erschienen in:

    Advances in Mechatronics, Manufacturing, and Mechanical Engineering ; Kapitel : 17 ; 161-171

    Lecture Notes in Mechanical Engineering

    Erscheinungsdatum :

    2020-08-06

    Format / Umfang :

    11 pages

    ISBN :

    978-981-15-7309-5 , 978-981-15-7308-8

    ISSN :

    2195-4364 , 2195-4356

    DOI :

    https://doi.org/10.1007/978-981-15-7309-5_17

    Medientyp :

    Aufsatz/Kapitel (Buch)

    Format :

    Elektronische Ressource

    Sprache :

    Englisch

    Schlagwörter :

    Sign language , Convolutional Neural Networks , Deep learning Engineering , Control, Robotics, Mechatronics , Machine Learning , Machinery and Machine Elements , Manufacturing, Machines, Tools, Processes , Computational Intelligence

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