A Survey on Writer Identification and Recognition Methods with a Special Focus on Cultural Heritage

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dc.citation.spage9708
dc.citation.volume3246/05
dc.contributor.authorĆosović, Marijana
dc.contributor.authorJanković Babić, Radmila
dc.contributor.authorAmelio, Alessia
dc.date.accessioned2024-02-02T08:29:30Z
dc.date.available2024-02-02T08:29:30Z
dc.date.issued2022
dc.description.abstractThis paper reviews the state-of-the-art contributions for writer identification and recognition with a special focus on applications in the domain of cultural heritage. The task of writer recognition has only recently been recognized as a problem that can be solved by the methods available in the computer vision domain. A number of researchers have explored the performance of deep learning and transfer learning techniques for writer identification in historical documents, and for this purpose various datasets have been used, including the Avila Bible dataset, Historical-WI, HisFragIR20, IAM, HWDB and others. This paper analyses relevant methods used for writer identification and recognition in historical and medieval documents. It also makes a distinction between classification based on words, patches, or whole pages. The results indicate that the current literature supports using deep learning and transfer learning methods, as they are found to achieve the highest performance.
dc.identifier.urihttps://vaseljena.ues.rs.ba/handle/123456789/949
dc.language.isoen
dc.publisherCEUR Workshop Proceedings
dc.subjectWriter Recognition, Survey, Image Recognition, Cultural heritage
dc.titleA Survey on Writer Identification and Recognition Methods with a Special Focus on Cultural Heritage
dc.typeArticle
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Електротехнички факултет [Научни радови] / Faculty of Electrical Engineering [Scientific papers]