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The NoisyOffice Database: A Corpus To Train Supervised Machine Learning Filters For Image Processing

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The NoisyOffice Database: A Corpus To Train Supervised Machine Learning Filters For Image Processing

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dc.contributor.author Castro-Bleda, Maria Jose es_ES
dc.contributor.author España Boquera, Salvador es_ES
dc.contributor.author Pastor Pellicer, Joan es_ES
dc.contributor.author ZAMORA MARTÍNEZ, FRANCISCO JULIÁN es_ES
dc.date.accessioned 2020-12-12T04:31:47Z
dc.date.available 2020-12-12T04:31:47Z
dc.date.issued 2020-11 es_ES
dc.identifier.issn 0010-4620 es_ES
dc.identifier.uri http://hdl.handle.net/10251/156936
dc.description.abstract [EN] This paper presents the `NoisyOffice¿ database. It consists of images of printed text documents with noise mainly caused by uncleanliness from a generic office, such as coffee stains and footprints on documents or folded and wrinkled sheets with degraded printed text. This corpus is intended to train and evaluate supervised learning methods for cleaning, binarization and enhancement of noisy images of grayscale text documents. As an example, several experiments of image enhancement and binarization are presented by using deep learning techniques. Also, double-resolution images are also provided for testing super-resolution methods. The corpus is freely available at UCI Machine Learning Repository. Finally, a challenge organized by Kaggle Inc. to denoise images, using the database, is described in order to show its suitability for benchmarking of image processing systems. es_ES
dc.description.sponsorship This research was undertaken as part of the project TIN2017-85854-C4-2-R, jointly funded by the Spanish MINECO and FEDER founds. es_ES
dc.language Inglés es_ES
dc.publisher Oxford University Press es_ES
dc.relation.ispartof The Computer Journal es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Optical character recognition es_ES
dc.subject Image processing es_ES
dc.subject Binarization es_ES
dc.subject Denoising es_ES
dc.subject Super resolution es_ES
dc.subject Machine learning es_ES
dc.subject Neural networks es_ES
dc.subject Deep learning es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title The NoisyOffice Database: A Corpus To Train Supervised Machine Learning Filters For Image Processing es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1093/comjnl/bxz098 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-85854-C4-2-R/ES/AMIC-UPV: ANALISIS AFECTIVO DE INFORMACION MULTIMEDIA CON COMUNICACION INCLUSIVA Y NATURAL/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Castro-Bleda, MJ.; España Boquera, S.; Pastor Pellicer, J.; Zamora Martínez, FJ. (2020). The NoisyOffice Database: A Corpus To Train Supervised Machine Learning Filters For Image Processing. The Computer Journal. 63(11):1658-1667. https://doi.org/10.1093/comjnl/bxz098 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1093/comjnl/bxz098 es_ES
dc.description.upvformatpinicio 1658 es_ES
dc.description.upvformatpfin 1667 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 63 es_ES
dc.description.issue 11 es_ES
dc.relation.pasarela S\403568 es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
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