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Authors: Bing Quan Huang and Tahar Kechadi

Affiliation: University College Dublin, Ireland

Keyword(s): Recurrent neural network, gradient descent method, learning algorithms, Markov chain model, fuzzy logic, feature extraction, context layers.

Related Ontology Subjects/Areas/Topics: Advanced Applications of Fuzzy Logic ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Industrial Applications of Artificial Intelligence ; Machine Perception: Vision, Speech, Other ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: This paper presents an innovative hybrid approach for online recognition of handwritten symbols. This approach is composed of two main techniques. The first technique, based on fuzzy logic, deals with feature extraction from a handwritten stroke and the second technique, a recurrent neural network, uses the features as an input to recognise the symbol. In this paper we mainly focuss our study on the second technique. We proposed a new recurrent neural network architecture associated with an efficient learning algorithm. We describe the network and explain the relationship between the network and the Markov chains. Finally, we implemented the approach and tested it using benchmark datasets extracted from the Unipen database.

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Paper citation in several formats:
Quan Huang, B. and Kechadi, T. (2005). A RECURRENT NEURAL NETWORK RECOGNISER FOR ONLINE RECOGNITION OF HANDWRITTEN SYMBOLS. In Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 972-8865-19-8; ISSN 2184-4992, SciTePress, pages 27-34. DOI: 10.5220/0002514200270034

@conference{iceis05,
author={Bing {Quan Huang}. and Tahar Kechadi.},
title={A RECURRENT NEURAL NETWORK RECOGNISER FOR ONLINE RECOGNITION OF HANDWRITTEN SYMBOLS},
booktitle={Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2005},
pages={27-34},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002514200270034},
isbn={972-8865-19-8},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - A RECURRENT NEURAL NETWORK RECOGNISER FOR ONLINE RECOGNITION OF HANDWRITTEN SYMBOLS
SN - 972-8865-19-8
IS - 2184-4992
AU - Quan Huang, B.
AU - Kechadi, T.
PY - 2005
SP - 27
EP - 34
DO - 10.5220/0002514200270034
PB - SciTePress