Isolated Video-Based Arabic Sign Language Recognition Using Convolutional and Recursive Neural Networks

2021 ◽  
Author(s):  
Abdelbasset Boukdir ◽  
Mohamed Benaddy ◽  
Ayoub Ellahyani ◽  
Othmane El Meslouhi ◽  
Mustapha Kardouchi
Keyword(s):  
Neural Networks ◽  
Sign Language ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Recursive Neural Networks ◽  
Arabic Sign Language

scholarly journals Arabic Sign Language Recognition through Deep Neural Networks Fine-Tuning

2020 ◽  
Vol 16 (05) ◽  
pp. 71
Author(s):  
Yaser Saleh ◽  
Ghassan Farid Issa
Keyword(s):  
Neural Networks ◽  
Sign Language ◽  
The Body ◽  
Fine Tuning ◽  
Communication Tool ◽  
Learning Networks ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Deep Convolutional Neural Networks ◽  
Arabic Sign Language

<p class="0abstract">Sign Language is considered the main communication tool for deaf or hearing impaired people.  It is a visual language that uses hands and other parts of the body to provide people who are in need to full access of communication with the world.  Accordingly, the automation of sign language recognition has become one of the important applications in the areas of Artificial Intelligence and Machine learning.  Specifically speaking, Arabic sign language recognition has been studied and applied using various intelligent and traditional approaches, but with few attempts to improve the process using deep learning networks.  This paper utilizes transfer learning and fine tuning deep convolutional neural networks (CNN) to improve the accuracy of recognizing 32 hand gestures from the Arabic sign language.  The proposed methodology works by creating models matching the VGG16 and the ResNet152 structures, then, the pre-trained model weights are loaded into the layers of each network, and finally, our own soft-max classification layer is added as the final layer after the last fully connected layer.  The networks were fed with normal 2D images of the different Arabic Sign Language data, and was able to provide accuracy of nearly 99%.</p>

scholarly journals Arabic Sign Language Recognition System Using 2D Hands and Body Skeleton Data

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 59612-59627
Author(s):  
Mohamed A. Bencherif ◽  
Mohammed Algabri ◽  
Mohamed A. Mekhtiche ◽  
Mohammed Faisal ◽  
Mansour Alsulaiman ◽  
...  
Keyword(s):  
Sign Language ◽  
Recognition System ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Arabic Sign Language

KArSL

2021 ◽  
Vol 20 (1) ◽  
pp. 1-19
Author(s):  
Ala Addin I. Sidig ◽  
Hamzah Luqman ◽  
Sabri Mahmoud ◽  
Mohamed Mohandes
Keyword(s):  
Deep Learning ◽  
Sign Language ◽  
Markov Models ◽  
Body Language ◽  
Arab Countries ◽  
Microsoft Kinect ◽  
Classification Model ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Arabic Sign Language

Sign language is the major means of communication for the deaf community. It uses body language and gestures such as hand shapes, lib patterns, and facial expressions to convey a message. Sign language is geography-specific, as it differs from one country to another. Arabic Sign language is used in all Arab countries. The availability of a comprehensive benchmarking database for ArSL is one of the challenges of the automatic recognition of Arabic Sign language. This article introduces KArSL database for ArSL, consisting of 502 signs that cover 11 chapters of ArSL dictionary. Signs in KArSL database are performed by three professional signers, and each sign is repeated 50 times by each signer. The database is recorded using state-of-art multi-modal Microsoft Kinect V2. We also propose three approaches for sign language recognition using this database. The proposed systems are Hidden Markov Models, deep learning images’ classification model applied on an image composed of shots of the video of the sign, and attention-based deep learning captioning system. Recognition accuracies of these systems indicate their suitability for such a large number of Arabic signs. The techniques are also tested on a publicly available database. KArSL database will be made freely available for interested researchers.

Mexican sign language recognition using normalized moments and artificial neural networks

2014 ◽  
Author(s):  
J-Francisco Solís-V. ◽  
Carina Toxqui-Quitl ◽  
David Martínez-Martínez ◽  
Margarita H.-G.
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sign Language ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Artificial Neural
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