Abstract
This article studies the implementation of a handwritten character recognition task using neural networks. Two logic neural network models axe employed to classify the Essex dataset, which comprises real-world hand-written characters. To reduce the underlying dataset variation, several pre-processing approaches are investigated. This allows the comparison of the network models on the basis of their classification accuracy for datasets with different characteristics.
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© 1998 Springer-Verlag Wien
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Tambouratzis, G. (1998). Optimising Handwritten-Character Recognition with Logic Neural Networks. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6492-1_31
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DOI: https://doi.org/10.1007/978-3-7091-6492-1_31
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83087-1
Online ISBN: 978-3-7091-6492-1
eBook Packages: Springer Book Archive