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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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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

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Título: The NoisyOffice Database: A Corpus To Train Supervised Machine Learning Filters For Image Processing
Autor: Castro-Bleda, Maria Jose España Boquera, Salvador Pastor Pellicer, Joan ZAMORA MARTÍNEZ, FRANCISCO JULIÁN
Entidad UPV: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Fecha difusión:
Resumen:
[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 ...[+]
Palabras clave: Optical character recognition , Image processing , Binarization , Denoising , Super resolution , Machine learning , Neural networks , Deep learning
Derechos de uso: Reserva de todos los derechos
Fuente:
The Computer Journal. (issn: 0010-4620 )
DOI: 10.1093/comjnl/bxz098
Editorial:
Oxford University Press
Versión del editor: https://doi.org/10.1093/comjnl/bxz098
Código del Proyecto:
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/
Agradecimientos:
This research was undertaken as part of the project TIN2017-85854-C4-2-R, jointly funded by the Spanish MINECO and FEDER founds.
Tipo: Artículo

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