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

Ryo Inoue, Hidehisa Nakayama, Nei Kato

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

1 Citation (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 languageEnglish
Title of host publicationProceedings - 18th International Conference on Pattern Recognition, ICPR 2006
Pages1094-1097
Number of pages4
DOIs
Publication statusPublished - 2006 Dec 1
Event18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China
Duration: 2006 Aug 202006 Aug 24

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2
ISSN (Print)1051-4651

Other

Other18th International Conference on Pattern Recognition, ICPR 2006
Country/TerritoryChina
CityHong Kong
Period06/8/2006/8/24

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Fingerprint

Dive into the research topics of 'Historical hand-written string recognition by non-linear discriminant analysis using kernel feature selection'. Together they form a unique fingerprint.

Cite this