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Neural network language models for off-line handwriting recognition

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Neural network language models for off-line handwriting recognition

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Zamora Martínez, FJ.; Frinken, V.; España Boquera, S.; Castro-Bleda, MJ.; Fischer, A.; Bunke, H. (2014). Neural network language models for off-line handwriting recognition. Pattern Recognition. 47(4):1642-1652. https://doi.org/10.1016/j.patcog.2013.10.020

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/44432

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Title: Neural network language models for off-line handwriting recognition
Author: Zamora Martínez, Francisco Julián Frinken, V. España Boquera, Salvador Castro-Bleda, Maria Jose Fischer, A. Bunke, Horst
UPV Unit: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Issued date:
[EN] Unconstrained off-line continuous handwritten text recognition is a very challenging task which has been recently addressed by different promising techniques. This work presents our latest contribution to this task, ...[+]
Subjects: Handwritten text recognition (HTR) , Language models (LMs) , Neural networks (NNs) , Neural network language model (NN LM) , Bidirectional long short-term memory neural networks (BLSTM) , Hybrid HMM/ANN models , ROVER combination
Copyrigths: Cerrado
Pattern Recognition. (issn: 0031-3203 )
DOI: 10.1016/j.patcog.2013.10.020
Publisher version: https://doi.org/10.1016/j.patcog.2013.10.020
Project ID:
The authors wish to acknowledge the anonymous reviewers for their detailed and helpful comments to the paper. We also thank Alex Graves for kindly providing us with the BLSTM Neural Network source code. This work has been ...[+]
Type: Artículo

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