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dc.contributor.author | Prieto, José Ramón![]() |
es_ES |
dc.contributor.author | Andrés, José![]() |
es_ES |
dc.contributor.author | GRANELL, EMILIO![]() |
es_ES |
dc.contributor.author | Sánchez Peiró, Joan Andreu![]() |
es_ES |
dc.contributor.author | Vidal, Enrique![]() |
es_ES |
dc.date.accessioned | 2024-07-01T18:37:31Z | |
dc.date.available | 2024-07-01T18:37:31Z | |
dc.date.issued | 2023-08 | es_ES |
dc.identifier.issn | 0167-8655 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/205655 | |
dc.description.abstract | [EN] Document Image Understanding is a demanding Pattern Recognition problem that requires complex recognition models. This problem is even more difficult for document images with complicated layouts like tables, where the reading order is often intrinsically ambiguous, and consequently, the context is generally ambiguous as well. In this paper, we compare two machine learning approaches for extracting information in pre-printed historical tables with handwritten information. We analyze the performance of each approach at each step of the extraction process over different corpora, up to a realistic scenario where documents with different table layouts written by different hands are used. The results are good in general and show that a model based on Multilayer Perceptrons yields better results on more homogeneous documents, while another model based on Graph Neural Networks generalizes better on heterogeneous corpora. | es_ES |
dc.description.sponsorship | Work partially supported by : the Universitat Politecnica de Valencia under grant FPI-I/SP20190010 (Spain), the Siman-casSearch project as Grant PID2020-116813RB-I00a funded by MCIN/AEI/10.13039/501100011033, grant DIN2021-011820 funded by MCIN/AEI/10.13039/501100011033, the valgrAI - Valencian Graduate School and Research Network of Artificial Intelligence and the Generalitat Valenciana, and co-funded by the European Union. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Pattern Recognition Letters | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Structured handwritten documents | es_ES |
dc.subject | Information extraction | es_ES |
dc.subject | Neural networks | es_ES |
dc.subject.classification | LENGUAJES Y SISTEMAS INFORMATICOS | es_ES |
dc.title | Information Extraction in Handwritten Historical Logbooks | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.patrec.2023.06.008 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AGENCIA ESTATAL DE INVESTIGACION//PID2020-116813RB-I00//SEARCHING IN THE SIMANCA ARCHIVE/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/UPV//SP20190010/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI//DIN2021-011820/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Politécnica Superior de Gandia - Escola Politècnica Superior de Gandia | es_ES |
dc.description.bibliographicCitation | Prieto, JR.; Andrés, J.; Granell, E.; Sánchez Peiró, JA.; Vidal, E. (2023). Information Extraction in Handwritten Historical Logbooks. Pattern Recognition Letters. 172:128-136. https://doi.org/10.1016/j.patrec.2023.06.008 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.patrec.2023.06.008 | es_ES |
dc.description.upvformatpinicio | 128 | es_ES |
dc.description.upvformatpfin | 136 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 172 | es_ES |
dc.relation.pasarela | S\495643 | es_ES |
dc.contributor.funder | AGENCIA ESTATAL DE INVESTIGACION | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
dc.contributor.funder | Universitat Politècnica de València | es_ES |
dc.contributor.funder | Valencian Graduate School and Research Network of Artificial Intelligence | es_ES |