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

Authors:
Zhen-Long BAI;Qiang HUO
Affiliations:
University of Hong Kong, Hong Kong, China;University of Hong Kong, Hong Kong, China
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Year:
2006

Citing 0
Cited 3

Modeling inverse covariance matrices by expansion of tied basis matrices for online handwritten Chinese character recognition

Pattern Recognition
Automatic Online Educational Game Content Creation by Identifying Similar Chinese Characters with Radical Extraction and Graph Matching Algorithms

International Journal of Distance Education Technologies
A recognition system for online handwritten tibetan characters

GREC'11 Proceedings of the 9th international conference on Graphics Recognition: new trends and challenges

Quantified Score

Hi-index	0.00

Visualization

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.