Clustering with projection distance and pseudo bayes discriminant function for handwritten numeral recognition

Meng Shi, Wataru Oyama, Tetsushi Wakabayashi, Fumitaka Kimura

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

1 Citation (Scopus)

Abstract

This paper investigates the usage of the projection distance and the pseudo Bayes discriminant function as the distortion measure for handwritten numeral clustering problem. These distortion measures are not only refer to the mean vectors but also related to the covariance matixes of subclasses, thus, the distribution of subclasses are reflected on the obtained clusters, and the accuracy of recognition can be improved. A series of evaluation experiments are performed on the handwritten numeral database NIST SD3 and SD7. The experimental results show that the recognition rate has been increased from 97.35% to 98.35%, which is one the highest rates ever reported for the database.

Original languageEnglish
Title of host publicationProceedings - 6th International Conference on Document Analysis and Recognition, ICDAR 2001
PublisherIEEE Computer Society
Pages1007-1011
Number of pages5
ISBN (Electronic)0769512631, 0769512631, 0769512631
DOIs
Publication statusPublished - Jan 1 2001
Event6th International Conference on Document Analysis and Recognition, ICDAR 2001 - Seattle, United States
Duration: Sep 10 2001Sep 13 2001

Publication series

NameProceedings of the International Conference on Document Analysis and Recognition, ICDAR
Volume2001-January
ISSN (Print)1520-5363

Other

Other6th International Conference on Document Analysis and Recognition, ICDAR 2001
CountryUnited States
CitySeattle
Period9/10/019/13/01

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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  • Cite this

    Shi, M., Oyama, W., Wakabayashi, T., & Kimura, F. (2001). Clustering with projection distance and pseudo bayes discriminant function for handwritten numeral recognition. In Proceedings - 6th International Conference on Document Analysis and Recognition, ICDAR 2001 (pp. 1007-1011). [953937] (Proceedings of the International Conference on Document Analysis and Recognition, ICDAR; Vol. 2001-January). IEEE Computer Society. https://doi.org/10.1109/ICDAR.2001.953937