Abstract
In this paper, we introduce an efficient clustering based coarse-classifier for a Chinese handwriting recognition system to accelerate the recognition procedure. We define a candidate-cluster-number for each character. The defined number indicates the within-class diversity of a character in the feature space. Based on the candidate-cluster-number of each character, we use a candidate-refining module to reduce the size of the candidate set of the coarse-classifier. Experiments show that the method effectively reduces the output set size of the coarse-classifier, while keeping the same coverage probability of the candidate set. The method has a low computation-complexity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & Sons, New York (2001)
Bradley, R.S., Fayyad, U.: Refining Initial Points for K-means Clustering. In: Proc. 15th Int’l Conf. Machine Learning Madison, Wisconsin, USA, pp. 91–99 (1999)
Lloyd, S.P.: Least Squares Quantization in PCM. IEEE Trans. Information Theory 28, 129–137 (1982)
MacQueen, J.: Some Methods for Classification and Analysis of Multivariate Observations. In: Proc. Fifth Berkeley Symp. Math. Statistics and Probability, Berkeley, California, USA, vol. 1, pp. 281–296 (1967)
Forgey, E.: Cluster Analysis of Multivariate Data Efficiency vs. Interpretability of Classification. Biometrics 21, 768 (1965)
Lin, T.Z., Fan, K.C.: Coarse Classification of On-Line Chinese Characters via Structure Feature-Based Method. Pattern Recognition 27, 1365–1377 (1994)
Lay, S.R., Lee, C.H., Cheng, N.J., Tseng, C.C.: On-Line Chinese Character Recognition with Effective Candidate Radical and Candidate Character Selections. Pattern Recognition 29, 1647–1659 (1996)
Tang, Y.Y., Tu, L.T., Liu, J., Lee, S.W.: Offline Recognition of Chinese Handwriting by Multifeature and Multilevel Classification. IEEE Trans. PAMI 21, 258–262 (1999)
Linde, Y., Buzo, A., Gray, R.M.: An algorithm for vector quantizer design. IEEE Trans. On Comm COM-28(1), 84–95 (1980)
Kanungo, T., Mount, D.M., Netanyahu, N.S.: An Efficient k-Means Clustering Algorithm: Analysis and Implementation. IEEE Trans. PAMI 24, 881–982 (2002)
Yang, Y., Velek, O., Nakagawa, M.: Accelerating Large Character Set Recognition using Pivots. In: Proc. 7th ICDAR, Edinburgh, Scotland, vol. 4C, pp. 262–267 (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guo, FJ., Zhen, LX., Ge, Y., Zhang, Y. (2008). An Efficient Candidate Set Size Reduction Method for Coarse-Classification in Chinese Handwriting Recognition. In: Doermann, D., Jaeger, S. (eds) Arabic and Chinese Handwriting Recognition. SACH 2006. Lecture Notes in Computer Science, vol 4768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78199-8_9
Download citation
DOI: https://doi.org/10.1007/978-3-540-78199-8_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78198-1
Online ISBN: 978-3-540-78199-8
eBook Packages: Computer ScienceComputer Science (R0)