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A learning process to the identification of feature points on Chinese characters

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2 Author(s)

The paper describes a novel stroke extraction approach to identify the feature points of a character, using line-filtering and learning-based techniques. The line-filtering technique based on convolution operations with a set of one-dimensional (1D) Gabor templates efficiently extracts the stroke segments from noisy and degraded characters. Furthermore, the relationship between endpoints of stroke segments is modeled as junction structure during a learning process. Finally, each endpoint is identified as a feature point to determine the junction structure by the learning-based technique, rather than rule-based techniques with manual rule creation. Experimental results indicate that the learning-based technique can generalize learning knowledge to identify 1200 feature points with an average identification rate of 93.58% for test set, using k-fold cross-validation testing.

Published in:

Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on  (Volume:33 ,  Issue: 3 )

Date of Publication:

May 2003

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