Online graphic symbol recognition using neural network and ARG matching

Bing Yang; Changhua Li; Weixing Xie

doi:10.1117/12.441680

20 September 2001 Online graphic symbol recognition using neural network and ARG matching

Bing Yang, Changhua Li, Weixing Xie

Proceedings Volume 4555, Neural Network and Distributed Processing; (2001) https://doi.org/10.1117/12.441680
Event: Multispectral Image Processing and Pattern Recognition, 2001, Wuhan, China

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.

Citation Download Citation

Bing Yang, Changhua Li, and Weixing Xie "Online graphic symbol recognition using neural network and ARG matching", Proc. SPIE 4555, Neural Network and Distributed Processing, (20 September 2001); https://doi.org/10.1117/12.441680

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