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Towards Portuguese Sign Language Identification Using Deep Learning

dc.contributor.authorRicardo Vardasca, PhD, ASIS, FRPS
dc.contributor.authorMartinho, Domingos
dc.date.accessioned2022-01-04T14:56:44Z
dc.date.available2022-01-04T14:56:44Z
dc.date.issued2021
dc.description.abstractIn Portugal there are above 80,000 people with hearing impairment with the need to communicate through the sign language. Equal opportunities and social inclusion are the major concerns of the current society. It is aim of this research to create and evaluate a Deep Learning model that using a dataset with images of characters in Portuguese sign language can identify the gesture of a user, recognizing it. For model training, 5826 representative samples of the characters ‘C’, ‘I’, ‘L’, ‘U’ and ‘Y’ in Portuguese sign language. The Deep Learning model is based on a convolutional neural network. The model evaluated using the sample allowed for an accuracy of 98.5%, which is considered as a satisfactory result. However, there are two gaps: the existence of datasets with the totality of the alphabet in the Portuguese sign language and with the various representations of movement that each word has at the layout of letters. Using the proposed model with more complete datasets would allow to develop more inclusive user interfaces and equal opportunities for users with auditory difficulties.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1007/978-3-030-90241-4_6pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.26/38582
dc.language.isoengpt_PT
dc.subjectDeep learningpt_PT
dc.subjectInclusion user interfacespt_PT
dc.subjectPortuguese sign languagept_PT
dc.titleTowards Portuguese Sign Language Identification Using Deep Learningpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage80pt_PT
oaire.citation.startPage70pt_PT
oaire.citation.volume1485pt_PT
person.familyNameVardasca
person.familyNameMartinho
person.givenNameRicardo
person.givenNameDomingos
person.identifierR-001-FFR
person.identifier.ciencia-id9F17-FD5F-E767
person.identifier.ciencia-idDF14-D953-4D04
person.identifier.orcid0000-0003-4217-2882
person.identifier.orcid0000-0002-5887-4814
person.identifier.ridJ-4948-2013
person.identifier.scopus-author-id24491279800
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication33602b11-6c79-40f9-a768-d7c792bc2d57
relation.isAuthorOfPublicationc5d125b8-0dad-4298-807c-a24cd9780b32
relation.isAuthorOfPublication.latestForDiscovery33602b11-6c79-40f9-a768-d7c792bc2d57

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