Historical hand-written string recognition by non-linear discriminant analysis using kernel feature selection

Ryo Inoue; Hidehisa Nakayama; Nei Kato

doi:10.1109/ICPR.2006.629

Historical hand-written string recognition by non-linear discriminant analysis using kernel feature selection

Ryo Inoue, Hidehisa Nakayama, Nei Kato

Tohoku University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

In this paper, we propose a method to compose a classifier by non-linear discriminant analysis using kernel method combined with kernel feature selection for holistic recognition of historical hand-written string. Through experiments using historical hand-written string database HCD2, we show that our approach can obtain high recognition accuracy comparable to that of individual character recognition.

Original language	English
Title of host publication	Proceedings - 18th International Conference on Pattern Recognition, ICPR 2006
Pages	1094-1097
Number of pages	4
DOIs	https://doi.org/10.1109/ICPR.2006.629
Publication status	Published - 2006
Event	18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China Duration: 2006 Aug 20 → 2006 Aug 24

Publication series

Name	Proceedings - International Conference on Pattern Recognition
Volume	2
ISSN (Print)	1051-4651

Conference

Conference	18th International Conference on Pattern Recognition, ICPR 2006
Country/Territory	China
City	Hong Kong
Period	06/8/20 → 06/8/24

Access to Document

10.1109/ICPR.2006.629

Cite this

Inoue, R., Nakayama, H., & Kato, N. (2006). Historical hand-written string recognition by non-linear discriminant analysis using kernel feature selection. In Proceedings - 18th International Conference on Pattern Recognition, ICPR 2006 (pp. 1094-1097). Article 1699399 (Proceedings - International Conference on Pattern Recognition; Vol. 2). https://doi.org/10.1109/ICPR.2006.629

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title = "Historical hand-written string recognition by non-linear discriminant analysis using kernel feature selection",

abstract = "In this paper, we propose a method to compose a classifier by non-linear discriminant analysis using kernel method combined with kernel feature selection for holistic recognition of historical hand-written string. Through experiments using historical hand-written string database HCD2, we show that our approach can obtain high recognition accuracy comparable to that of individual character recognition.",

author = "Ryo Inoue and Hidehisa Nakayama and Nei Kato",

year = "2006",

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language = "English",

isbn = "0769525210",

series = "Proceedings - International Conference on Pattern Recognition",

pages = "1094--1097",

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note = "18th International Conference on Pattern Recognition, ICPR 2006 ; Conference date: 20-08-2006 Through 24-08-2006",

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Inoue, R, Nakayama, H & Kato, N 2006, Historical hand-written string recognition by non-linear discriminant analysis using kernel feature selection. in Proceedings - 18th International Conference on Pattern Recognition, ICPR 2006., 1699399, Proceedings - International Conference on Pattern Recognition, vol. 2, pp. 1094-1097, 18th International Conference on Pattern Recognition, ICPR 2006, Hong Kong, China, 06/8/20. https://doi.org/10.1109/ICPR.2006.629

Historical hand-written string recognition by non-linear discriminant analysis using kernel feature selection. / Inoue, Ryo; Nakayama, Hidehisa; Kato, Nei.
Proceedings - 18th International Conference on Pattern Recognition, ICPR 2006. 2006. p. 1094-1097 1699399 (Proceedings - International Conference on Pattern Recognition; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Historical hand-written string recognition by non-linear discriminant analysis using kernel feature selection

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