A study of nonlinear shape normalization for Online handwritten Chinese character recognition: Dot density vs. line density equalization

被引：0

作者：

Bai, Zhen-Long ^{[1
]}

Huo, Qiang ^{[1
]}

机构：

[1] Univ Hong Kong, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China

来源：

18TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2, PROCEEDINGS | 2006年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：921 / +

页数：2

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