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dc.contributor.authorBecerra, Guillermo
dc.contributor.authorMora, Marco
dc.contributor.authorAubin, Verónica
dc.contributor.authorHernández-García, Ruber
dc.date.accessioned2023-03-08T13:39:26Z
dc.date.available2023-03-08T13:39:26Z
dc.date.issued2022
dc.identifier.urihttp://repositorio.ucm.cl/handle/ucm/4499
dc.description.abstractThis paper proposes a new method for writer identification based on small fragments of handwritten text randomly obtained from a paragraph. The main contribution of this work is to show that small fragments carry enough biometric information for writer identification. A second contribution is the creation of 2 repositories of images of handwritten text from 50 writers. The first one is made up of 4 text paragraphs of 64 words in high resolution per writer. The second one contains more than 700 thousand fragments of text per writer. Experiments were conducted with different Convolutional Neural Networks, considering the VGG-16, VGG19, InceptionV3, ResNet-50 and MobileNetV2m models. 2 classification schemes were implemented. First, the classification of individual fragments and, second, the classification of groups of fragments. The best results were obtained using groups of fragments, achieving accuracy of 96% on the identification of a text with the same content and of 87% on the identification of the writer considering a text with different content.es_CL
dc.language.isoenes_CL
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/*
dc.sourceInternational Conference on Automation/XXV Congress of the Chilean Association of Automatic Control (ICA-ACCA), Curicó, Chile, 1-6es_CL
dc.subjectImage resolutiones_CL
dc.subjectBiometrics (access control)es_CL
dc.subjectConvolutional neural networkses_CL
dc.titleOff-line writer verification based on small segments of handwritten text and convolutional neural networkses_CL
dc.typeArticlees_CL
dc.ucm.facultadFacultad de Ciencias de la Ingenieríaes_CL
dc.ucm.indexacionScopuses_CL
dc.ucm.uriieeexplore.ieee.org/document/10006220es_CL
dc.ucm.doidoi.org/10.1109/ICA-ACCA56767.2022.10006220es_CL


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Atribución-NoComercial-SinDerivadas 3.0 Chile
Excepto si se señala otra cosa, la licencia de la publicación se describe como Atribución-NoComercial-SinDerivadas 3.0 Chile