| This paper deals with accuracy improvement of handwritten character recognition by the GLVQ (generalized learning vector quantization). In literature 3 , the way of combining the FDA (Fisher discriminant analysis) and the GLVQ was investi gated and evaluated to be e#ective for handwritten Chinese character recognition employing the minimum Euclidian distance classifier. In this paper, the projection distance and the modified projection distance are employed besides the Euclidi an distance, and handwritten numerals as well as Chinese characters are used for the evaluation test. The result of experiment shows that the learning of refer ence vectors by GLVQ improves the recognition accuracy of not only the Euclidian distance classifier but also the projection distance classifier and the modified pro jection distance classifier. The highest accuracy (98.41%) for the Chinese character recognition was obtained when the FDA, GLVQ and the modified projection dis tance were employed. The highest accuracy (99.36%) for the numeral recognition was obtained when the GLVQ and the modified projection distance were employed. |
In: L.R.B. Schomaker and L.G. Vuurpijl (Eds.)
Proceedings of the Seventh International Workshop on Frontiers
in Handwriting Recognition, September 11-13 2000, Amsterdam,
Nijmegen: International Unipen Foundation,
ISBN 90-76942-01-3
pp. 271-280.