Handwritten character recognition using elastic matching based on a class-dependent deformation model

Seiichi Uchida, Hiroaki Sakoe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

For handwritten character recognition, a new elastic image matching (EM) technique based on a class-dependent deformation model is proposed. In the deformation model, any deformation of a class is described by a linear combination of eigen-deformations, which are intrinsic deformation directions of the class. The eigen-deformations can be estimated statistically from the actual deformations of handwritten characters. Experimental results show that the proposed technique can attain higher recognition rates than conventional EM techniques based on class-independent deformation models. The results also show the superiority of the proposed technique over those conventional EM techniques in computational efficiency.

Original languageEnglish
Title of host publicationProceedings - 7th International Conference on Document Analysis and Recognition, ICDAR 2003
PublisherIEEE Computer Society
Pages163-167
Number of pages5
ISBN (Electronic)0769519601
DOIs
Publication statusPublished - Jan 1 2003
Event7th International Conference on Document Analysis and Recognition, ICDAR 2003 - Edinburgh, United Kingdom
Duration: Aug 3 2003Aug 6 2003

Publication series

NameProceedings of the International Conference on Document Analysis and Recognition, ICDAR
Volume2003-January
ISSN (Print)1520-5363

Other

Other7th International Conference on Document Analysis and Recognition, ICDAR 2003
CountryUnited Kingdom
CityEdinburgh
Period8/3/038/6/03

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All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

Cite this

Uchida, S., & Sakoe, H. (2003). Handwritten character recognition using elastic matching based on a class-dependent deformation model. In Proceedings - 7th International Conference on Document Analysis and Recognition, ICDAR 2003 (pp. 163-167). [1227652] (Proceedings of the International Conference on Document Analysis and Recognition, ICDAR; Vol. 2003-January). IEEE Computer Society. https://doi.org/10.1109/ICDAR.2003.1227652