Implementing Fuzziness in the Pattern Recognition Process for Improving the Classification of the Patterns Being Recognised

  • Sapna Singh
  • Daya Shankar Singh
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 247)


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.

Index Terms

Backpropagation network Artificial Neural Network fuzzy logic Knowledge Base 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Computer Science & Engineering DepartmentMadan Mohan Malaviya Engineering CollegeGorakhpurIndia

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