Noisy Hangul character recognition with fuzzy tree classifier

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Decision trees have been applied to solve a wide range of pattern recognition problems. In a tree classifier, a sequence of decision rules are used to assign an unknown sample to a pattern class. The main advantage of a decision tree over a single stage classifier is that the complex global decision making process can be divided into a number of simpler and local decisions at different levels of the tree. At each stage of the decision process, the feature subset best suited for that classification task can be selected. It can be shown that this approach provides better results than the use of the best feature subset for a single decision classifier. In addition, in large set problems where the number of classes is very large, the tree classifier can make a global decision much more quickly than the single stage classifier. However, a major weak point of a tree classifier is its error accumulation effect when the number of classes is very large. To overcome this difficulty, a fuzzy tree classifier with the following characteristics is implemented: (1) fuzzy logic search is used to find all `possible correct classes,' and some similarity measures are used to determine the `most probable class;' (2) global training is applied to generate extended terminals in order to enhance the recognition rate; (3) both the training and search algorithms have been given a lot of flexibility, to provide tradeoffs between error and rejection rates, and between the recognition rate and speed. Experimental results for the recognition of 520 most frequently used noisy Hangul character categories revealed a very high recognition rate of 99.8 percent and very high speed of 100 samples/sec, when the program was written in C and run on general purpose SUN4 SPARCstation 2.

Original languageEnglish
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherPubl by Int Soc for Optical Engineering
Pages127-136
Number of pages10
Volume1661
ISBN (Print)0819408158
Publication statusPublished - 1992
Externally publishedYes
EventMachine Vision Applications in Character Recognition and Industrial Inspection - San Jose, CA, USA
Duration: 1992 Feb 101992 Feb 12

Other

OtherMachine Vision Applications in Character Recognition and Industrial Inspection
CitySan Jose, CA, USA
Period92/2/1092/2/12

Fingerprint

character recognition
Character recognition
classifiers
Classifiers
Decision trees
set theory
education
decision making
tradeoffs
pattern recognition
rejection
Fuzzy logic
Pattern recognition
logic
flexibility
Decision making
high speed

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics

Cite this

Lee, S. W. (1992). Noisy Hangul character recognition with fuzzy tree classifier. In Proceedings of SPIE - The International Society for Optical Engineering (Vol. 1661, pp. 127-136). Publ by Int Soc for Optical Engineering.

Noisy Hangul character recognition with fuzzy tree classifier. / Lee, Seong Whan.

Proceedings of SPIE - The International Society for Optical Engineering. Vol. 1661 Publ by Int Soc for Optical Engineering, 1992. p. 127-136.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Lee, SW 1992, Noisy Hangul character recognition with fuzzy tree classifier. in Proceedings of SPIE - The International Society for Optical Engineering. vol. 1661, Publ by Int Soc for Optical Engineering, pp. 127-136, Machine Vision Applications in Character Recognition and Industrial Inspection, San Jose, CA, USA, 92/2/10.
Lee SW. Noisy Hangul character recognition with fuzzy tree classifier. In Proceedings of SPIE - The International Society for Optical Engineering. Vol. 1661. Publ by Int Soc for Optical Engineering. 1992. p. 127-136
Lee, Seong Whan. / Noisy Hangul character recognition with fuzzy tree classifier. Proceedings of SPIE - The International Society for Optical Engineering. Vol. 1661 Publ by Int Soc for Optical Engineering, 1992. pp. 127-136
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