A STROKE-BASED NEURO-FUZZY SYSTEM FOR HANDWRITTEN CHINESE CHARACTER RECOGNITION.

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Bibliographic Details
Title:	A STROKE-BASED NEURO-FUZZY SYSTEM FOR HANDWRITTEN CHINESE CHARACTER RECOGNITION.
Authors:	Lin, Jue-Wen, Lee, Shie-Jue, Yang, Hsin-Tai
Source:	Applied Artificial Intelligence. Jul2001, Vol. 15 Issue 6, p561-586. 26p. 29 Diagrams, 3 Charts, 7 Graphs.
Subjects:	Chinese writing, Computer software
Abstract:	In this article, a stroke-based neuro-fuzzy system for off-line recognition of handwritten Chinese characters is proposed. The system consists of three main components: stroke extraction, feature extraction, and recognition. Stroke extraction applies a run-length-based method to extract strokes from the image of a given character. Various fuzzy features of the extracted strokes, including slope, length, location, and cross relation, are obtained by the feature extraction module. An ART-based neural network, using a two-stage training algorithm, is used to recognize characters. This system extracts strokes in only two passes, and is free from the presence of spurious and thick strokes. The neural model used provides a fast convergence rate. Nodes are allowed to be shared to reduce the size of the resulting network. Features need not be classified in advance by the user. Furthermore, the architecture of the network is self-constructed without the intervention of the user. Experiments have shown that this system is effective. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	In this article, a stroke-based neuro-fuzzy system for off-line recognition of handwritten Chinese characters is proposed. The system consists of three main components: stroke extraction, feature extraction, and recognition. Stroke extraction applies a run-length-based method to extract strokes from the image of a given character. Various fuzzy features of the extracted strokes, including slope, length, location, and cross relation, are obtained by the feature extraction module. An ART-based neural network, using a two-stage training algorithm, is used to recognize characters. This system extracts strokes in only two passes, and is free from the presence of spurious and thick strokes. The neural model used provides a fast convergence rate. Nodes are allowed to be shared to reduce the size of the resulting network. Features need not be classified in advance by the user. Furthermore, the architecture of the network is self-constructed without the intervention of the user. Experiments have shown that this system is effective. [ABSTRACT FROM AUTHOR]
ISSN:	08839514
DOI:	10.1080/088395101753199579