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dc.contributorEscuela de Ingenierias Industrial, Informática y Aeroespaciales_ES
dc.contributor.authorBlanco Medina, Pablo
dc.contributor.authorFidalgo Fernández, Eduardo 
dc.contributor.authorAlegre Gutiérrez, Enrique 
dc.contributor.authorAlaiz Rodríguez, Rocío 
dc.contributor.authorJáñez Martino, Francisco 
dc.contributor.authorBonnici, Alexandra
dc.contributor.otherIngenieria de Sistemas y Automaticaes_ES
dc.date2020-10-16
dc.date.accessioned2024-05-06T09:12:01Z
dc.date.available2024-05-06T09:12:01Z
dc.identifier.citationBlanco-Medina, P., Fidalgo, E., Alegre, E., Alaiz-Rodríguez, R., Jáñez-Martino, F., & Bonnici, A. (2020). Rectification and super-resolution enhancements for forensic text recognition. Sensors (Switzerland), 20(20), 1-17. https://doi.org/10.3390/S20205850es_ES
dc.identifier.otherhttps://www.mdpi.com/1424-8220/20/20/5850es_ES
dc.identifier.urihttps://hdl.handle.net/10612/20387
dc.description.abstract[EN] Retrieving text embedded within images is a challenging task in real-world settings. Multiple problems such as low-resolution and the orientation of the text can hinder the extraction of information. These problems are common in environments such as Tor Darknet and Child Sexual Abuse images, where text extraction is crucial in the prevention of illegal activities. In this work, we evaluate eight text recognizers and, to increase the performance of text transcription, we combine these recognizers with rectification networks and super-resolution algorithms. We test our approach on four state-of-the-art and two custom datasets (TOICO-1K and Child Sexual Abuse (CSA)-text, based on text retrieved from Tor Darknet and Child Sexual Exploitation Material, respectively). We obtained a 0.3170 score of correctly recognized words in the TOICO-1K dataset when we combined Deep Convolutional Neural Networks (CNN) and rectification-based recognizers. For the CSA-text dataset, applying resolution enhancements achieved a final score of 0.6960. The highest performance increase was achieved on the ICDAR 2015 dataset, with an improvement of 4.83% when combining the MORAN recognizer and the Residual Dense resolution approach. We conclude that rectification outperforms super-resolution when applied separately, while their combination achieves the best average improvements in the chosen datasets.es_ES
dc.languageenges_ES
dc.publisherMDPIes_ES
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectCibernéticaes_ES
dc.subjectInformáticaes_ES
dc.subject.otherText spottinges_ES
dc.subject.otherText recognitiones_ES
dc.subject.otherSuper-resolutiones_ES
dc.subject.otherTor Darknetes_ES
dc.subject.otherComputer forensicses_ES
dc.titleRectification and Super-Resolution Enhancements for Forensic Text Recognitiones_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.identifier.doi10.3390/s20205850
dc.description.peerreviewedSIes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.essn1424-8220
dc.journal.titleSensorses_ES
dc.volume.number20es_ES
dc.issue.number20es_ES
dc.page.initial5850es_ES
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES
dc.subject.unesco3304.05 Sistemas de Reconocimiento de Caractereses_ES
dc.subject.unesco1207.03 Cibernéticaes_ES
dc.subject.unesco1203.17 Informáticaes_ES
dc.description.projectInstituto Nacional de Ciberseguridades_ES
dc.description.projectThis research has been funded with support from the European Commission under the 4NSEEK project with Grant Agreement 821966. This publication reflects the views only of the author, and the European Commission cannot be held responsible for any use that may be made of the information contained therein.es_ES
dc.description.projectThis research has been supported by the grant ’Ayudas para la realización de estudios de doctorado en el marco del programa propio de investigación de la Universidad de León Convocatoria 2018’ and by the framework agreement between Universidad de León and INCIBE (Spanish National Cybersecurity Institute) under Addendum 01. We acknowledge NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.es_ES


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Atribución 4.0 Internacional
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