Bing Quan Huang, Tahar Kechadi


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 Harvard Style

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, pages 27-34. DOI: 10.5220/0002514200270034

in Bibtex Style

author={Bing Quan Huang and Tahar Kechadi},
booktitle={Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 2: ICEIS,},

in EndNote Style

JO - Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 2: ICEIS,
SN - 972-8865-19-8
AU - Quan Huang B.
AU - Kechadi T.
PY - 2005
SP - 27
EP - 34
DO - 10.5220/0002514200270034