Latif Ghazanfar, Mohammad Nazeeruddin, Alghazo Jaafar, AlKhalaf Roaa, AlKhalaf Rawan
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.
Data Brief. 2019 Feb 23;23:103777. doi: 10.1016/j.dib.2019.103777. eCollection 2019 Apr.
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.
为与手语识别相关的研究开发了一个带有完整标注的阿拉伯手语（ArSL）图像数据集。该数据集将为研究人员提供机会，利用机器学习、计算机视觉和深度学习算法，为聋人和听力障碍者研究和开发自动化系统。其贡献在于提供了一个大型的、带有完整标注的阿拉伯手语（ArSL）数据集，该数据集已公开提供给所有研究人员且免费使用。这个名为ArSL2018的数据集包含从40名不同年龄组的参与者那里收集的、用于32个阿拉伯手语手势和字母的54,049张图像。图像中存在不同的尺寸和变化，可以使用预处理技术来清除噪声、使图像居中等等。该数据集在https://data.mendeley.com/datasets/y7pckrw6z2/1上公开提供。
