College of Computer Engineering and Sciences, Prince Mohammad Bin Fahd University, Al Khobar, Saudi Arabia; Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan, Malaysia; Corresponding author. College of Computer Engineering and Sciences, Prince Mohammad Bin Fahd University, Al Khobar, Saudi Arabia.
Nazeeruddin Mohammad
College of Computer Engineering and Sciences, Prince Mohammad Bin Fahd University, Al Khobar, Saudi Arabia
Jaafar Alghazo
College of Computer Engineering and Sciences, Prince Mohammad Bin Fahd University, Al Khobar, Saudi Arabia
Roaa AlKhalaf
College of Computer Engineering and Sciences, Prince Mohammad Bin Fahd University, Al Khobar, Saudi Arabia
Rawan AlKhalaf
College of Computer Engineering and Sciences, Prince Mohammad Bin Fahd University, Al Khobar, Saudi Arabia
A fully-labelled dataset of Arabic Sign Language (ArSL) images is developed for research related to sign language recognition. The dataset will provide researcher the opportunity to investigate and develop automated systems for the deaf and hard of hearing people using machine learning, computer vision and deep learning algorithms. The contribution is a large fully-labelled dataset for Arabic Sign Language (ArSL) which is made publically available and free for all researchers. The dataset which is named ArSL2018 consists of 54,049 images for the 32 Arabic sign language sign and alphabets collected from 40 participants in different age groups. Different dimensions and different variations were present in images which can be cleared using pre-processing techniques to remove noise, center the image, etc. The dataset is made available publicly at https://data.mendeley.com/datasets/y7pckrw6z2/1.
