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Recognition of handwritten numerals by Quantum Neural Network with fuzzy features

Abstract.

This paper describes a new kind of neural network – Quantum Neural Network (QNN) – and its application to the recognition of handwritten numerals. QNN combines the advantages of neural modelling and fuzzy theoretic principles. Novel experiments have been designed for in-depth studies of applying the QNN to both real data and confusing images synthesized by morphing. Tests on synthesized data examine QNN's fuzzy decision boundary with the intention to illustrate its mechanism and characteristics, while studies on real data prove its great potential as a handwritten numeral classifier and the special role it plays in multi-expert systems. An effective decision-fusion system is proposed and a high reliability of 99.10% has been achieved.

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Received October 26, 1998 / Revised January 9, 1999

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Zhou, J., Gan, Q., Krzyżak, A. et al. Recognition of handwritten numerals by Quantum Neural Network with fuzzy features. IJDAR 2, 30–36 (1999). https://doi.org/10.1007/s100320050034

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  • Key words: Quantum Neural Network – Handwritten numeral recognition – Fuzzy classification – Morphing – Decision-fusion system