학술논문
Handwritten digit recognition based on prototypes created by Euclidean distance
Document Type
Conference
Author
Source
Proceedings 1999 International Conference on Information Intelligence and Systems (Cat. No.PR00446) Information intelligence and systems Information Intelligence and Systems, 1999. Proceedings. 1999 International Conference on. :320-323 1999
Subject
Language
Abstract
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%).