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Proceedings Paper

Handwritten Chinese character recognition based on supervised competitive learning neural network and block-based relative fuzzy feature extraction
Author(s): Limin Sun; Shuanhu Wu
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Paper Abstract

Offline handwritten chinese character recognition (HCCR) is still a difficult problem because of its large stroke changes, writing anomaly, and the difficulty for obtaining its stroke ranking information. Generally, offline HCCR can be divided into two procedures: feature extraction for capturing handwritten Chinese character information and feature classifying for character recognition. In this paper, we proposed a new chinese character recognition algorithm. In feature extraction part, we adopted elastic mesh dividing method for extracting the block features and its relative fuzzy features that utilized the relativities between different strokes and distribution probability of a stroke in its neighbor sub-blocks. In recognition part, we constructed a classifier based on a supervisory competitive learning algorithm to train competitive learning neural network with the extracted features set. Experimental results show that the performance of our algorithm is encouraging and can be comparable to other algorithms.

Paper Details

Date Published: 23 February 2005
PDF: 6 pages
Proc. SPIE 5673, Applications of Neural Networks and Machine Learning in Image Processing IX, (23 February 2005); doi: 10.1117/12.587156
Show Author Affiliations
Limin Sun, Yantai Univ. (China)
Shuanhu Wu, Yantai Univ. (China)

Published in SPIE Proceedings Vol. 5673:
Applications of Neural Networks and Machine Learning in Image Processing IX
Nasser M. Nasrabadi; Syed A. Rizvi, Editor(s)

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