The Recognition of Printed Chinese Characters using Probabilistic VQ Networks and hierarchical Structure


The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 4, No. 7, pp. 1881-1892, Jul. 1997
10.3745/KIPSTE.1997.4.7.1881,   PDF Download:

Abstract

This paper proposes the method for recognition of printed chinese characters by probabilistic VQ networks and multi-stage recognizer has hierarchical structure. We use modular neural networks, because it is difficult to construct a large-scale neural network. Problems in this procedure are replaced by probabilistic neural network model. And, Confused Characters which have significant ratio of miss-classification are reclassified using the entropy theory. The experimental object consists of 4,619 chinese characters within the KSC5601 code except the same shape but different code. We have 99.33% recognition rate to the training data, and 92.83% to the test data. And, the recognition speed of system is 4-5 characters per second. Then, these results demonstrate the usefulness of our work.


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Cite this article
[IEEE Style]
L. J. Hoon, S. Y. Woo, N. J. Chan, "The Recognition of Printed Chinese Characters using Probabilistic VQ Networks and hierarchical Structure," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 4, no. 7, pp. 1881-1892, 1997. DOI: 10.3745/KIPSTE.1997.4.7.1881.

[ACM Style]
Lee Jang Hoon, Shon Young Woo, and NamKung Jae Chan. 1997. The Recognition of Printed Chinese Characters using Probabilistic VQ Networks and hierarchical Structure. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 4, 7, (1997), 1881-1892. DOI: 10.3745/KIPSTE.1997.4.7.1881.