IDEAS home Printed from https://ideas.repec.org/a/epw/ejeng0/v8y2023i5id63092.html

Glove-Based Classification of Hand Gestures for Arabic Sign Language Using Faster-CNN

Author

Listed:
  • Ahmed M. D. E. Hassanein

    (National Research Centre (NRC), Egypt)

  • Sarah H. A. Mohamed

    (School of Engineering, October University for Modern Sciences and Arts (MSA), Egypt)

  • Kamran Pedram

    (University of Greenwich, United Kingdom)

Abstract

Recently, American Sign Language has been widely researched to help disabled people to communicate with others. However; the Arabic Sign Language “ASL” has received much less attention. This paper has proposed a smart glove which has been designed using flex sensors to collect a dataset about hand gestures applying ASL. The dataset is composed of resistance and voltage measurements for the bending of the fingers to represent alpha-numeric characters. The measurements are manipulated using normalization and zero referencing methods to create the dataset. A Convolutional Neural Network ‘CNN’ composed of twenty-one layers is proposed. The dataset is used to train the CNN, and the Accuracy and Loss parameters are used to characterize its success. The dataset is classified with an average success rate of 95% based on the classification accuracy. Loss has decreased from 3 to less than 0.5. The proposed CNN layers have classified ASL characters with a reasonable degree of accuracy.

Suggested Citation

  • Ahmed M. D. E. Hassanein & Sarah H. A. Mohamed & Kamran Pedram, 2023. "Glove-Based Classification of Hand Gestures for Arabic Sign Language Using Faster-CNN," European Journal of Engineering and Technology Research, European Open Science, vol. 8(5), pages 31-35, September.
    • Handle: RePEc:epw:ejeng0:v:8:y:2023:i:5:id:63092
    DOI: 10.24018/ejeng.2023.8.5.3092
    as

    Download full text from publisher

    File URL: https://eu-opensci.org/index.php/ejeng/article/view/63092
    File Function: Abstract page
    Download Restriction: no
    File URL: https://eu-opensci.org/index.php/ejeng/article/download/63092/12948
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.24018/ejeng.2023.8.5.3092?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:epw:ejeng0:v:8:y:2023:i:5:id:63092. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Support (email available below). General contact details of provider: https://eu-opensci.org/index.php/ejeng .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.