Abstract
This paper presents an innovative hybrid approach for online recognition of handwritten symbols. The approach is composed of two main techniques. Firstly, fuzzy rules are used to extract a meaningful set of features from a handwritten symbol, and secondly a recurrent neural network uses the feature set as input to recognise the symbol. The extracted feature set is a set of basic shapes capturing what is distinctive about each symbol, thus making the network’s classification task easier. We propose a new recurrent neural network architecture, associated with an efficient learning algorithm derived from the gradient descent method. We describe the network and explain the relationship between the network and the Markov chains. The approach has achieved high recognition rates using benchmark datasets from the Unipen database.
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Fitzgerald, J.A., Huang, B.Q., Kechadi, T. (2005). An Efficient Hybrid Approach for Online Recognition of Handwritten Symbols. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds) MICAI 2005: Advances in Artificial Intelligence. MICAI 2005. Lecture Notes in Computer Science(), vol 3789. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11579427_86
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DOI: https://doi.org/10.1007/11579427_86
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