An applied coarse classification scheme and analysis

Author

Li, Hong-rui ; Yang, Fang ; Zuo, Li-Na ; Tian, Xue-dong

Author_Institution

Coll. of Math. & Comput. Sci., Hebei Univ., Baoding

Volume

3

fYear

2008

fDate

12-15 July 2008

Firstpage

1740

Lastpage

1743

Abstract

An applied coarse classification scheme for handwritten Chinese character is presented in this paper. Four-side code feature is employed as coarse feature and RBF neural network is used as classifier in this experiment. In contrast to Euclidean distance as the measurement of similarity used in conventional method, RBF (radial basis function) neural network is better to fit the data of each class. In this way the precision rate is up to 93.20%. Analyzing the misclassified characters, overlap area classification method is applied in experiment and precision rate is up to 96.18%. Experimental results show that proposed method is applied and has satisfying performance on coarse classification of handwritten Chinese character.

Keywords

feature extraction; handwritten character recognition; image classification; radial basis function networks; RBF neural network; applied coarse classification scheme; four-side code feature; handwritten Chinese character classification; overlap area classification method; radial basis function; Character recognition; Cybernetics; Educational institutions; Euclidean distance; Feature extraction; Handwriting recognition; Machine learning; Mathematics; Neural networks; Shape; Coarse classification; Handwritten Chinese character; Overlap area classification; RBF neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2008 International Conference on

Conference_Location

Kunming

Print_ISBN

978-1-4244-2095-7

Electronic_ISBN

978-1-4244-2096-4

Type

conf

DOI

10.1109/ICMLC.2008.4620686

Filename

4620686