scholarly journals Comparison of Four SVM Classifiers Used with Depth Sensors to Recognize Arabic Sign Language Words

Computers ◽  
2017 ◽  
Vol 6 (2) ◽  
pp. 20 ◽  
Author(s):  
Miada Almasre ◽  
Hana Al-Nuaim
Keyword(s):  
Sign Language ◽  
Depth Sensors ◽  
Arabic Sign Language

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.

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