A Study of Nonlinear Shape Normalization for Online Handwritten Chinese Character Recognition: Dot Density vs. Line Density Equalization

Author

BAI, Zhen-Long ; Huo, Qiang

Author_Institution

Dept. of Comput. Sci., Hong Kong Univ.

Volume

2

fYear

0

fDate

0-0 0

Firstpage

921

Lastpage

924

Abstract

Nonlinear shape normalization (NSN) approaches based on line density equalization have been the most popular choice for both offline and online handwritten Chinese character recognition (HCCR). However, in a recent study of using 8-directional features for online HCCR, we discovered that an NSN approach based on dot density equalization achieved a much better performance than that of an NSN approach based on line density equalization. In this paper, we present the details of the NSN approaches we studied for online HCCR, and report the comparative experimental results using an in-house developed Chinese handwriting corpus as well as the popular Nakayosi and Kuchibue Japanese character databases. We also present an improved NSN approach based on the equalization of dot densities derived from blurred character image that can be used for offline HCCR

Keywords

feature extraction; handwritten character recognition; natural languages; Chinese handwriting corpus; Japanese character databases; blurred character image; dot density equalization; line density equalization; nonlinear shape normalization; online handwritten Chinese character recognition; Character generation; Character recognition; Computer science; Feature extraction; Image databases; Image generation; Pattern recognition; Shape; US Department of Transportation; Writing;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2006. ICPR 2006. 18th International Conference on

Conference_Location

Hong Kong

ISSN

1051-4651

Print_ISBN

0-7695-2521-0

Type

conf

DOI

10.1109/ICPR.2006.179

Filename

1699356