Abstract
Correctly classifying and recognizing objects are essentially a knowledge based process. As the unpredictability of the objects to be identified increases, the process becomes increasingly difficult, even if the objects come from a small set. This variability has been taken into account by devising a fuzzy logic based approach using threshold value feature. In this paper, two methods of encoding knowledge in a system are covered-neural network and fuzzy logic-as they are currently applied to offline hand written character recognition, which is subject to high degrees of unpredictability. This paper proposes a recognition system that classifies a class of recognised patterns i.e. “partially recognised” applying fuzziness in the obtained patterns after training with backpropagation neural network and checks for the validation of the concept being proposed.
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Singh, S., Singh, D.S. (2014). Implementing Fuzziness in the Pattern Recognition Process for Improving the Classification of the Patterns Being Recognised. In: Satapathy, S., Udgata, S., Biswal, B. (eds) Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2013. Advances in Intelligent Systems and Computing, vol 247. Springer, Cham. https://doi.org/10.1007/978-3-319-02931-3_5
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DOI: https://doi.org/10.1007/978-3-319-02931-3_5
Publisher Name: Springer, Cham
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