A learning algorithm detecting the similar Chinese characters´ boundary based on unequal-contraction of dimension

Author

Jinying, Chen ; Yijiang, Jin ; Shaoping, Ma

Author_Institution

Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China

Volume

4

fYear

2000

fDate

2000

Firstpage

2765

Abstract

In a recognition system of off-line handwritten Chinese characters, which has a proper recognition rate, improving the recognition rate of similar characters is the key to raising the whole recognition rate. K-L transformation, linear projection, and nonlinear projection are used to visualize the distribution of high-dimension Chinese character vectors. By making comparison experiments between very-similar and very-different Chinese characters, we summarize the distribution characteristic of the high-dimension similar Chinese characters. Utilizing the Mahalanobis distance to measure the similarity of characters and according to the results of statistical experiments, we present a learning algorithm to determine the similar Chinese characters´ boundary based on unequal-contraction of dimension

Keywords

Karhunen-Loeve transforms; handwritten character recognition; learning (artificial intelligence); vectors; K-L transformation; Mahalanobis distance; distribution characteristic; handwritten Chinese characters; high-dimension Chinese character vectors; learning algorithm; linear projection; nonlinear projection; recognition rate; Character recognition; Computer science; Handwriting recognition; Intelligent systems; Laboratories; Visualization;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on

Conference_Location

Hefei

Print_ISBN

0-7803-5995-X

Type

conf

DOI

10.1109/WCICA.2000.862563

Filename

862563