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Proceedings Paper

Online graphic symbol recognition using neural network and ARG matching
Author(s): Bing Yang; Changhua Li; Weixing Xie
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Paper Abstract

This paper proposes a novel method for on-line recognition of line-based graphic symbol. The input strokes are usually warped into a cursive form due to the sundry drawing style, and classifying them is very difficult. To deal with this, an ART-2 neural network is used to classify the input strokes. It has the advantages of high recognition rate, less recognition time and forming classes in a self-organized manner. The symbol recognition is achieved by an Attribute Relational Graph (ARG) matching algorithm. The ARG is very efficient for representing complex objects, but computation cost is very high. To over come this, we suggest a fast graph matching algorithm using symbol structure information. The experimental results show that the proposed method is effective for recognition of symbols with hierarchical structure.

Paper Details

Date Published: 20 September 2001
PDF: 6 pages
Proc. SPIE 4555, Neural Network and Distributed Processing, (20 September 2001); doi: 10.1117/12.441680
Show Author Affiliations
Bing Yang, Xidian Univ. (China)
Changhua Li, Xidian Univ. (China)
Weixing Xie, Xidian Univ. (China)

Published in SPIE Proceedings Vol. 4555:
Neural Network and Distributed Processing
Xubang Shen; Jianguo Liu, Editor(s)

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