[1] Lu Da**, Qian Yiping, Xie Mingpei, Pu Wei, et al. An Approach to Unsupervised Character ClassificationBased on Similarity Measure in Fuzzy Model* [J]. Journal of Southeast University (English Edition), 2002, 18 (4): 370-376. [doi:10.3969/j.issn.1003-7985.2002.04.017]

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An Approach to Unsupervised Character ClassificationBased on Similarity Measure in Fuzzy Model^*()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

18

Issue:

2002 4

Page:

370-376

Research Field:

Computer Science and Engineering

Publishing date:

2002-12-30

Info

Title:

An Approach to Unsupervised Character ClassificationBased on Similarity Measure in Fuzzy Model^*

Author(s):

Lu Da^1**;  Qian Yiping¹;  Xie Mingpei²;  Pu Wei¹

¹Department of Physics, Changshu College, Changshu 215500, China
²Department of Computer Science, Fudan University, Shanghai 200433, China

Keywords:

fuzzy model;  weighted fuzzy similarity measure;  unsupervised character classification;  matching algorithm;  classification hierarchy

PACS:

TP391

DOI:

10.3969/j.issn.1003-7985.2002.04.017

Abstract:

This paper presents a fuzzy logic approach to efficiently perform unsupervised character classification for improvement in robustness, correctness and speed of a character recognition system. The characters are first split into eight typographical categories. The classification scheme uses pattern matching to classify the characters in each category into a set of fuzzy prototypes based on a nonlinear weighted similarity function. The fuzzy unsupervised character classification, which is natural in the representation of prototypes for character matching, is developed and a weighted fuzzy similarity measure is explored.The characteristics of the fuzzy model are discussed and used in speeding up the classification process. After classification, the character recognition which is simply applied on a smaller set of the fuzzy prototypes, becomes much easier and less time-consuming.

References:

[1] Akiyama T, Hagita N. Automated entry system for printed documents[J]. Pattern Recognition, 1990, 23(11):1141-1154.
[2] Schurmann J, Bartneck N, et al. Document analysis-from pixels to contents[A]. In: Proceedings of IEEE[C]. 1992, 80(7): 1101-1119.
[3] Srihari S N. Computer text recognition and error correction[M]. Silver Spring, MD, USA:Computer Science Press, 1985.
[4] Impedovo S, Ottaviano L. Optical character recognition — a survey[J]. Pattern Recognition, 1991, 5(1, 2):1-24.
[5] Bokser M. Omni document technologies[A]. In: Proceeding of the IEEE[C]. 1992, 80(7):1066-1078.
[6] Lu Da, McCane B, Pu Wei. Character preclassification based on fuzzy typographical analysis[A]. In: Proceeding of the 6th International Conference on Document Analysis and Recognition[C]. Seattle, Washington, USA: IEEE Press, 2001. 74-78.
[7] Lu Da, Pu Wei, Xie Mingpei. Precise detection algorithm for locating the baseline of a text line[J]. Mini-Micro System, 2000, 21(7):726-728.(in Chinese)
[8] Prakash M, Murty M. Growing subspace pattern recognition methods and their neural-network models[J]. IEEE Transactions on Neural Networks, 1997, 8(1): 161-168.
[9] Lu Da, Xie Mingpei. A segmentation method of topographic approach for merged character images based on skeletonization[J]. Journal of Chinese Information Processing, 1999, 13(2): 40-45.(in Chinese)

Memo

Memo:

* The project supported by the China Scholarship Council Foundation(99832097)and the National Science of Jiangsu Education Commission(99KGB140009).
** Born in 1947, male, associate professor.

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