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Artificial Intelligence Technologies for Sign Language

AI technologies can play an important role in breaking down the communication barriers of deaf or hearing-impaired people with other communities, contributing significantly to their social inclusion. Recent advances in both sensing technologies and AI algorithms have paved the way for the developmen...

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Detalles Bibliográficos
Autores principales: Papastratis, Ilias, Chatzikonstantinou, Christos, Konstantinidis, Dimitrios, Dimitropoulos, Kosmas, Daras, Petros
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8434597/
https://www.ncbi.nlm.nih.gov/pubmed/34502733
http://dx.doi.org/10.3390/s21175843
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author Papastratis, Ilias
Chatzikonstantinou, Christos
Konstantinidis, Dimitrios
Dimitropoulos, Kosmas
Daras, Petros
author_facet Papastratis, Ilias
Chatzikonstantinou, Christos
Konstantinidis, Dimitrios
Dimitropoulos, Kosmas
Daras, Petros
author_sort Papastratis, Ilias
collection PubMed
description AI technologies can play an important role in breaking down the communication barriers of deaf or hearing-impaired people with other communities, contributing significantly to their social inclusion. Recent advances in both sensing technologies and AI algorithms have paved the way for the development of various applications aiming at fulfilling the needs of deaf and hearing-impaired communities. To this end, this survey aims to provide a comprehensive review of state-of-the-art methods in sign language capturing, recognition, translation and representation, pinpointing their advantages and limitations. In addition, the survey presents a number of applications, while it discusses the main challenges in the field of sign language technologies. Future research direction are also proposed in order to assist prospective researchers towards further advancing the field.
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spelling pubmed-84345972021-09-12 Artificial Intelligence Technologies for Sign Language Papastratis, Ilias Chatzikonstantinou, Christos Konstantinidis, Dimitrios Dimitropoulos, Kosmas Daras, Petros Sensors (Basel) Article AI technologies can play an important role in breaking down the communication barriers of deaf or hearing-impaired people with other communities, contributing significantly to their social inclusion. Recent advances in both sensing technologies and AI algorithms have paved the way for the development of various applications aiming at fulfilling the needs of deaf and hearing-impaired communities. To this end, this survey aims to provide a comprehensive review of state-of-the-art methods in sign language capturing, recognition, translation and representation, pinpointing their advantages and limitations. In addition, the survey presents a number of applications, while it discusses the main challenges in the field of sign language technologies. Future research direction are also proposed in order to assist prospective researchers towards further advancing the field. MDPI 2021-08-30 /pmc/articles/PMC8434597/ /pubmed/34502733 http://dx.doi.org/10.3390/s21175843 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Papastratis, Ilias
Chatzikonstantinou, Christos
Konstantinidis, Dimitrios
Dimitropoulos, Kosmas
Daras, Petros
Artificial Intelligence Technologies for Sign Language
title Artificial Intelligence Technologies for Sign Language
title_full Artificial Intelligence Technologies for Sign Language
title_fullStr Artificial Intelligence Technologies for Sign Language
title_full_unstemmed Artificial Intelligence Technologies for Sign Language
title_short Artificial Intelligence Technologies for Sign Language
title_sort artificial intelligence technologies for sign language
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8434597/
https://www.ncbi.nlm.nih.gov/pubmed/34502733
http://dx.doi.org/10.3390/s21175843
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