University of Bahrain
Scientific Journals

An Automatic Arabic Sign Language Recognition System based on Deep CNN: An Assistive System for the Deaf and Hard of Hearing

Show simple item record Latif, Ghazanfar Mohammad, Nazeeruddin AlKhalaf, Roaa AlKhalaf, Rawan Alghazo, Jaafar Khan, Majid 2020-07-01T20:02:57Z 2020-07-01T20:02:57Z 2020-07-01
dc.identifier.issn 2210-142X
dc.description.abstract People with disabilities have long been ignored. With the advancement of recent technologies, so many tools and software are designed for disabled people to improve their lives. In this research, the Arabic Sign Language (ArSL) recognition system is developed using the proposed architecture of the Deep Convolutional Neural Network (CNN). The aim is to help people with hearing problems to communicate with normal people. The proposed system recognizes the signs of the Arabic alphabet based on real-time user input. The Deep CNN architectures were trained and tested using a database of more than 50000 Arabic sign images collected from random participants of different age groups. Several experiments are performed with changing CNN architectural design parameters in order to get the best recognition rates. The experimental results show that the proposed Deep CNN architecture achieves an excellent accuracy of 97.6%, which is higher than the accuracy achieved by similar other studies. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International *
dc.rights.uri *
dc.subject Arabic Sign Language, Assistive System, Convolutional Neural Networks (CNN), Deep Learning, Sign Language Recognition, Assistive Technology, Deaf, Hard of Hearing en_US
dc.title An Automatic Arabic Sign Language Recognition System based on Deep CNN: An Assistive System for the Deaf and Hard of Hearing en_US
dc.type Article en_US
dc.volume 9 en_US
dc.issue 4 en_US
dc.pagestart 715 en_US
dc.pageend 724 en_US
dc.source.title International Journal of Computing and Digital Systems en_US
dc.abbreviatedsourcetitle IJCDS en_US

Files in this item

The following license files are associated with this item:

This item appears in the following Issue(s)

Show simple item record

Attribution-NonCommercial-NoDerivatives 4.0 International Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International

All Journals

Advanced Search


Administrator Account