Accuracy Improvement of Handwritten Character Recognition by Glvq

This paper deals with accuracy improvement of handwritten character recognition by the GLVQ (generalized learning vector quantization). In literature , the way of combining the FDA (Fisher discriminant analysis) and the GLVQ was investigated and evaluated to be effective 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 Euclidian distance, and handwritten numerals as well as Chinese characters are used for the evaluation test. The result of experiment shows that the learning of reference vectors by GLVQ improves the recognition accuracy of not only the Euclidian distance classifier but also the projection distance classifier and the modified projection distance classifier. The highest accuracy (98.41%) for the Chinese character recognition was obtained when the FDA, GLVQ and the modified projection distance were employed. The highest accuracy (99.36%) for the numeral recognition was obtained when the GLVQ and the modified projection distance were employed.