Match between normalization schemes and feature sets for handwritten Chinese character recognition

Qing Wang, Zheru Chi, David D. Feng, Rongchun Zhao

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)

Abstract

Because of the large number of Chinese characters and many different writing styles involved, the recognition of handwritten Chinese character remains a very challenging task. It is well recognized that a good feature set plays a key role in a successful recognition system. Shape normalization is as well an essential step toward achieving translation, scale, and rotation invariance in recognition. Many shape normalization methods and different feature sets have been proposed in the literature. This paper first reviews five commonly used shape normalization schemes and then discusses various feature extraction techniques usually used in handwritten Chinese character recognition. Based on numerous experiments conducted on 3,755 handwritten Chinese characters (GB23I2-80), we discuss the matches made between the normalization schemes and the features sets and suggest the best match between them in terms of classification performance. The nearest neighbor classifier was adopted in our experiments with templates obtained by using the K-means clustering algorithm.
Original languageEnglish
Title of host publicationProceedings - 6th International Conference on Document Analysis and Recognition, ICDAR 2001
PublisherIEEE Computer Society
Pages551-555
Number of pages5
Volume2001-January
ISBN (Electronic)0769512631
DOIs
Publication statusPublished - 1 Jan 2001
Event6th International Conference on Document Analysis and Recognition, ICDAR 2001 - Seattle, United States
Duration: 10 Sep 200113 Sep 2001

Conference

Conference6th International Conference on Document Analysis and Recognition, ICDAR 2001
Country/TerritoryUnited States
CitySeattle
Period10/09/0113/09/01

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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