Cargando…

ArASL: Arabic Alphabets Sign Language Dataset

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 vis...

Descripción completa

Detalles Bibliográficos
Autores principales: Latif, Ghazanfar, Mohammad, Nazeeruddin, Alghazo, Jaafar, AlKhalaf, Roaa, AlKhalaf, Rawan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6661066/
https://www.ncbi.nlm.nih.gov/pubmed/31372425
http://dx.doi.org/10.1016/j.dib.2019.103777
_version_ 1783439402340450304
author Latif, Ghazanfar
Mohammad, Nazeeruddin
Alghazo, Jaafar
AlKhalaf, Roaa
AlKhalaf, Rawan
author_facet Latif, Ghazanfar
Mohammad, Nazeeruddin
Alghazo, Jaafar
AlKhalaf, Roaa
AlKhalaf, Rawan
author_sort Latif, Ghazanfar
collection PubMed
description 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.
format Online
Article
Text
id pubmed-6661066
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-66610662019-08-01 ArASL: Arabic Alphabets Sign Language Dataset Latif, Ghazanfar Mohammad, Nazeeruddin Alghazo, Jaafar AlKhalaf, Roaa AlKhalaf, Rawan Data Brief Computer Science 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. Elsevier 2019-02-23 /pmc/articles/PMC6661066/ /pubmed/31372425 http://dx.doi.org/10.1016/j.dib.2019.103777 Text en © 2019 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Computer Science
Latif, Ghazanfar
Mohammad, Nazeeruddin
Alghazo, Jaafar
AlKhalaf, Roaa
AlKhalaf, Rawan
ArASL: Arabic Alphabets Sign Language Dataset
title ArASL: Arabic Alphabets Sign Language Dataset
title_full ArASL: Arabic Alphabets Sign Language Dataset
title_fullStr ArASL: Arabic Alphabets Sign Language Dataset
title_full_unstemmed ArASL: Arabic Alphabets Sign Language Dataset
title_short ArASL: Arabic Alphabets Sign Language Dataset
title_sort arasl: arabic alphabets sign language dataset
topic Computer Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6661066/
https://www.ncbi.nlm.nih.gov/pubmed/31372425
http://dx.doi.org/10.1016/j.dib.2019.103777
work_keys_str_mv AT latifghazanfar araslarabicalphabetssignlanguagedataset
AT mohammadnazeeruddin araslarabicalphabetssignlanguagedataset
AT alghazojaafar araslarabicalphabetssignlanguagedataset
AT alkhalafroaa araslarabicalphabetssignlanguagedataset
AT alkhalafrawan araslarabicalphabetssignlanguagedataset