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dc.contributor | Facultad de Veterinaria | es_ES |
dc.contributor.author | Blanco Medina, Pablo | |
dc.contributor.author | Fidalgo Fernández, Eduardo | |
dc.contributor.author | Alegre Gutiérrez, Enrique | |
dc.contributor.author | Al Nabki, Mohamed Wesam | |
dc.contributor.author | Chaves, Deisy | |
dc.contributor.editor | Calvo Rolle, José Luis | |
dc.contributor.editor | Casteleiro Roca, José Luis | |
dc.contributor.editor | Fernández Ibáñez, María Isabel | |
dc.contributor.editor | Fontenla Romero, Óscar | |
dc.contributor.editor | Jove Pérez, Esteban | |
dc.contributor.editor | Leira Rejas, Alberto José | |
dc.contributor.editor | López Vázquez, José Antonio | |
dc.contributor.editor | Loureiro Vázquez, Vanesa | |
dc.contributor.editor | Meizoso López, María Carmen | |
dc.contributor.editor | Pérez Castelo, Francisco Javier | |
dc.contributor.editor | Piñón Pazos, Andrés José | |
dc.contributor.editor | Quintián Pardo, Héctor | |
dc.contributor.editor | Rivas Rodríguez, Juan Manuel | |
dc.contributor.editor | Rodríguez Gómez, Benigno | |
dc.contributor.editor | Vega Vega, Rafael Alejandro | |
dc.contributor.other | Algebra | es_ES |
dc.date | 2019 | |
dc.date.accessioned | 2024-05-06T12:46:40Z | |
dc.date.available | 2024-05-06T12:46:40Z | |
dc.identifier.citation | Blanco Medina, P., Fidalgo Fernández, E., Alegre Gutiérrez, E., Wesam Al Nabki, M., & Chaves Sánchez, D. (2019). Enhancing text recognition on Tor Darknet images. En J. L. Calvo Rolle, J.-L. Casteleiro-Roca, I. Fernández-Ibáñez, Ó. Fontenla Romero, E. Jove Pérez, A. J. Leira-Rejas, J. A. López Vázquez, V. Loureiro-Vázquez, M.-C. Meizoso-López, F. J. Pérez Castelo, A. J. Piñón Pazos, H. Quintián Pardo, J. M. Rivas Rodríguez, B. A. Rodríguez Gómez, & R. A. Vega-Vega (eds.), XL Jornadas de Automática: libro de actas. Ferrol, 4-6 de septiembre de 2019. | es_ES |
dc.identifier.isbn | 978-84-9749-716-9 | es_ES |
dc.identifier.uri | https://hdl.handle.net/10612/20424 | |
dc.description.abstract | [EN] ext Spotting can be used as an approach to retrieve information found in images that cannot be obtained otherwise, by performing text detection rst and then recognizing the located text. Examples of images to apply this task on can be found in Tor network images, which contain information that may not be found in plain text. When comparing both stages, the latter performs worse due to the low resolution of the cropped areas among other problems. Focusing on the recognition part of the pipeline, we study the performance of ve recognition approaches, based on state-ofthe- art neural network models, standalone OCR, and OCR enhancements. We complement them using string-matching techniques with two lexicons and compare computational time on ve di erent datasets, including Tor network images. Our nal proposal achieved 39,70% precision of text recognition in a custom dataset of images taken from Tor domains | es_ES |
dc.language | eng | es_ES |
dc.publisher | Universidade da Coruña | es_ES |
dc.relation.ispartof | XL Jornadas de Automática: libro de actas. Ferrol, 4-6 de septiembre de 2019 | es_ES |
dc.rights | Atribución-NoComercial-CompartirIgual 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | * |
dc.subject | Informática | es_ES |
dc.subject.other | Text spotting | es_ES |
dc.subject.other | Text recognition | es_ES |
dc.subject.other | OCR | es_ES |
dc.subject.other | Cybersecurity | es_ES |
dc.subject.other | Tor darknet | es_ES |
dc.title | Enhancing text recognition on Tor Darknet images | es_ES |
dc.type | info:eu-repo/semantics/conferenceProceedings | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.page.initial | 828 | es_ES |
dc.page.final | 835 | es_ES |
dc.subject.unesco | 1207.03 Cibernética | es_ES |
dc.subject.unesco | 1203.17 Informática | es_ES |
dc.subject.unesco | 3304.05 Sistemas de Reconocimiento de Caracteres | es_ES |
dc.description.project | This research is supported by the framework agreement between Universidad de Le´on 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. This research has also 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 which may be made of the information contained therein | es_ES |
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