Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/107470
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dc.contributor.authorDíaz Cabrera, Moisésen_US
dc.contributor.authorCrispo, Gioeleen_US
dc.contributor.authorParziale, Antonioen_US
dc.contributor.authorMarcelli, Angeloen_US
dc.contributor.authorFerrer Ballester, Miguel Ángelen_US
dc.date.accessioned2021-06-09T10:47:35Z-
dc.date.available2021-06-09T10:47:35Z-
dc.date.issued2021en_US
dc.identifier.issn1989-1660en_US
dc.identifier.urihttp://hdl.handle.net/10553/107470-
dc.description.abstractThe order in which the trajectory is executed is a powerful source of information for recognizers. However, there is still no general approach for recovering the trajectory of complex and long handwriting from static images. Complex specimens can result in multiple pen-downs and in a high number of trajectory crossings yielding agglomerations of pixels (also known as clusters). While the scientific literature describes a wide range of approaches for recovering the writing order in handwriting, these approaches nevertheless lack a common evaluation metric. In this paper, we introduce a new system to estimate the order recovery of thinned static trajectories, which allows to effectively resolve the clusters and select the order of the executed pendowns. We evaluate how knowing the starting points of the pen-downs affects the quality of the recovered writing. Once the stability and sensitivity of the system is analyzed, we describe a series of experiments with three publicly available databases, showing competitive results in all cases. We expect the proposed system, whose code is made publicly available to the research community, to reduce potential confusion when the order of complex trajectories are recovered, and this will in turn make the trajectories recovered to be viable for further applications, such as velocity estimation.en_US
dc.languageengen_US
dc.relationGeneracion de Un Marco Unificado Para El Desarrollo de Patrones Biometricos de Comportamientoen_US
dc.relationModelado cinemático 3D para la caracterización del movimiento humano, animal y robóticoen_US
dc.relation.ispartofInternational Journal Of Interactive Multimedia And Artificial Intelligenceen_US
dc.sourceInternational Journal Of Interactive Multimedia And Artificial Intelligence [1989-1660],en_US
dc.subject120304 Inteligencia artificialen_US
dc.subject.otherCluster Resolutionen_US
dc.subject.otherComplex and Long Handwritingen_US
dc.subject.otherGood Continuity Criteriaen_US
dc.subject.otherWriting Order Recoveryen_US
dc.titleWriting Order Recovery in Complex and Long Static Handwritingen_US
dc.typeArticleen_US
dc.identifier.doi10.9781/ijimai.2021.04.003en_US
dc.identifier.issueIn Press-
dc.investigacionCienciasen_US
dc.type2Artículoen_US
dc.description.numberofpages14en_US
dc.utils.revisionen_US
dc.identifier.ulpgcNoen_US
dc.contributor.buulpgcBU-BASen_US
dc.description.jcr2,561
dc.description.jcrqQ2
item.fulltextCon texto completo-
item.grantfulltextopen-
crisitem.author.deptDepartamento de Física-
crisitem.author.deptIDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.orcid0000-0003-3878-3867-
crisitem.author.orcid0000-0002-2924-1225-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.fullNameDíaz Cabrera, Moisés-
crisitem.author.fullNameFerrer Ballester, Miguel Ángel-
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