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Recognition of Egyptian hieroglyphic texts through focused generic segmentation and cross-validation voting

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dc.contributor.authorFuentes Ferrer, Raúl
dc.contributor.authorDuque Domingo, Jaime
dc.contributor.authorHerrera Caro, Pedro Javier
dc.date.accessioned2025-04-24T07:42:25Z
dc.date.available2025-04-24T07:42:25Z
dc.date.issued2025-03
dc.descriptionThe registered version of this article, first published in “Applied Soft Computing, Volume 171, 2025", is available online at the publisher's website: Elsevier, https://doi.org/10.1016/j.asoc.2025.112793 La versión registrada de este artículo, publicado por primera vez en “Applied Soft Computing, Volume 171, 2025", está disponible en línea en el sitio web del editor: Elsevier, https://doi.org/10.1016/j.asoc.2025.112793
dc.description.abstractAncient Egyptian hieroglyphs form part of a complex language that has attracted the attention of Egyptologists, historians, and amateurs for centuries. In use for more than 3000 years, they consist of hundreds of symbols that can be transcribed into their Latin phonemes. Although there have been some previous works on the recognition of hieroglyphs through computer vision, this is a study of unprecedented depths and presents several unique contributions. On the one hand, we have created the largest and most complete dataset of existing Egyptian hieroglyphs to date, covering all the main symbols used on stelae. On the other, we have carried out a systematic analysis of detection, segmentation, and classification methods, focusing our research on a composite method of focused generic segmentation and classification with an ensemble model of ConvNeXt backbones using Cross-Validation Voting (CVV). Our trained model has been evaluated against several carved or painted stone stelae, obtaining excellent results. To the best of our knowledge, there is currently no other methodology capable of obtaining the classification results presented in this paper, and the method and the dataset presented represent a very significant advancement in the development of automated methods for reading Egyptian hieroglyphic texts.en
dc.description.versionversión final
dc.identifier.citationRaúl Fuentes-Ferrer, Jaime Duque-Domingo, Pedro Javier Herrera, Recognition of Egyptian hieroglyphic texts through focused generic segmentation and cross-validation voting, Applied Soft Computing, Volume 171, 2025, 112793, ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2025.112793
dc.identifier.doihttps://doi.org/10.1016/j.asoc.2025.112793
dc.identifier.issn1568-4946 | eISSN 1872-9681
dc.identifier.urihttps://hdl.handle.net/20.500.14468/26492
dc.journal.titleApplied Soft Computing
dc.journal.volume171
dc.language.isoen
dc.page.initial112793
dc.publisherELSEVIER
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentIngeniería de Software y Sistemas Informáticos
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject1203.17 Informática
dc.subject.keywordsegyptian hieroglyphsen
dc.subject.keywordsgeneric segmentationen
dc.subject.keywordsSAMen
dc.subject.keywordsCVVen
dc.subject.keywordsConvNeXten
dc.titleRecognition of Egyptian hieroglyphic texts through focused generic segmentation and cross-validation votingen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublicationd8a786c8-b4c1-42a1-8cdd-e7e0480024e0
relation.isAuthorOfPublication.latestForDiscoveryd8a786c8-b4c1-42a1-8cdd-e7e0480024e0
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