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Handwritten digit recognition based on prototypes created by Euclidean distance

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3 Author(s)
C. A. Perez ; Dept. of Electr. Eng., Chile Univ., Santiago, Chile ; C. M. Held ; P. R. Mollinger

Handwritten digits are recognized using prototypes created by a training algorithm based on the Euclidean distance. The subsequent classification of a handwritten digit is based on criteria considering the Euclidean distance to the prototypes. A training set of 2361 patterns is used to create the prototypes and a separate set of 1320 patterns is used to test the proposed method. The system performance is compared to two other known classification algorithms: a MLP (multilayer perceptron network), and SOM (self-organizing map) plus LVQ1 (a linear vector quantization algorithm). The proposed method reached a recognition rate of 93.5% when using the nearest-prototype criterion, and raised to 94.8% when using a nearest-prototype-voting criterion. It compared favorably with the MLP (91.8%) and SOM+LVQ1 (91.5%)

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Information Intelligence and Systems, 1999. Proceedings. 1999 International Conference on

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