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Handwriting recognition by using deep learning to extract meaningful features

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Handwriting recognition by using deep learning to extract meaningful features

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Pastor Pellicer, J.; Castro-Bleda, MJ.; España Boquera, S.; Zamora-Martinez, FJ. (2019). Handwriting recognition by using deep learning to extract meaningful features. AI Communications. 32(2):101-112. https://doi.org/10.3233/AIC-170562

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/138465

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Title: Handwriting recognition by using deep learning to extract meaningful features
Author:
UPV Unit: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Issued date:
Abstract:
[EN] Recent improvements in deep learning techniques show that deep models can extract more meaningful data directly from raw signals than conventional parametrization techniques, making it possible to avoid specific feature ...[+]
Subjects: Handwriting recognition , Deep learning , Convolutional neural networks
Copyrigths: Reserva de todos los derechos
Source:
AI Communications. (issn: 0921-7126 )
DOI: 10.3233/AIC-170562
Publisher:
IOS Press
Publisher version: https://doi.org/10.3233/AIC-170562
Thanks:
Work partially supported by the Spanish MINECO and FEDER founds under project TIN2017-85854-C4-2-R.
Type: Artículo

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