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Offline Features for Classifying Handwritten Math Symbols with Recurrent Neural Networks

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Offline Features for Classifying Handwritten Math Symbols with Recurrent Neural Networks

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Álvaro Muñoz, F.; Sánchez Peiró, JA.; Benedí Ruiz, JM. (2014). Offline Features for Classifying Handwritten Math Symbols with Recurrent Neural Networks. IEEE. doi:10.1109/ICPR.2014.507

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/68388

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Title: Offline Features for Classifying Handwritten Math Symbols with Recurrent Neural Networks
Author:
UPV Unit: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Issued date:
Abstract:
In mathematical expression recognition, symbol classification is a crucial step. Numerous approaches for recognizing handwritten math symbols have been published, but most of them are either an online approach or a ...[+]
Subjects: Handwritten Math Symbols , Recurrent Neural Networks
Copyrigths: Reserva de todos los derechos
ISBN: 9781479952083
Source:
International Conference on Pattern Recognition. (issn: 1051-4651 )
DOI: 10.1109/ICPR.2014.507
Publisher:
IEEE
Publisher version: http://dx.doi.org/10.1109/ICPR.2014.507
Conference name: 22nd International Conference on Pattern Recognition (ICPR 2014)
Conference place: Stockholm, Sweden
Conference date: August 24-28, 2015
Project ID: info:eu-repo/grantAgreement/EC/FP7/600707
Type: Comunicación en congreso

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