Abstract
This article describes a classifier of patterns based on a pre-processing system, located at the input of a recognition system using a Hopfield neural net, which recognises pattern transformed by translation, rotation and scaling. After a detailed description of components forming the chain of the pre-processing system, we present some results obtained by supplying the system input with handwritten characters deformed from rotation, scaling, and translation. The patterns gotten out of the the pre-processing module are furnished in input to the recognition net in order to evaluate the effectiveness of the pre-processing system itself. Besides the well known problems deriving from the scarce memorisation ability of the Hopfield net, is faced by a strategy that foresees the subdivision of the training patterns in groups minimally correlated.
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© 1999 Springer-Verlag Berlin Heidelberg
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Tascini, G., Montesanto, A., Fazzini, G., Puliti, P. (1999). Input pre-processing for transformation invariant pattern recognition. In: Mira, J., Sánchez-Andrés, J.V. (eds) Engineering Applications of Bio-Inspired Artificial Neural Networks. IWANN 1999. Lecture Notes in Computer Science, vol 1607. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0100506
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DOI: https://doi.org/10.1007/BFb0100506
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