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Reseach Article

Choosing Shape Features by means of Genetic Algorithms for Gylph-clustering of Historical Documents

by Jan-Hendrik Worch, Bjoern Gottfried
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 102 - Number 3
Year of Publication: 2014
Authors: Jan-Hendrik Worch, Bjoern Gottfried
10.5120/17792-8585

Jan-Hendrik Worch, Bjoern Gottfried . Choosing Shape Features by means of Genetic Algorithms for Gylph-clustering of Historical Documents. International Journal of Computer Applications. 102, 3 ( September 2014), 1-6. DOI=10.5120/17792-8585

@article{ 10.5120/17792-8585,
author = { Jan-Hendrik Worch, Bjoern Gottfried },
title = { Choosing Shape Features by means of Genetic Algorithms for Gylph-clustering of Historical Documents },
journal = { International Journal of Computer Applications },
issue_date = { September 2014 },
volume = { 102 },
number = { 3 },
month = { September },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume102/number3/17792-8585/ },
doi = { 10.5120/17792-8585 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:32:07.449006+05:30
%A Jan-Hendrik Worch
%A Bjoern Gottfried
%T Choosing Shape Features by means of Genetic Algorithms for Gylph-clustering of Historical Documents
%J International Journal of Computer Applications
%@ 0975-8887
%V 102
%N 3
%P 1-6
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The solution for a feature selection problem is presented in the field of document image processing. The choice of shape features for describing glyphs of historical documents is a non-trivial task since the variations of glyphs in different documents is innumerable. Hence, the manual selection of shape features would be a cumbersome task. To select a subset of features from a given set a genetic algorithm is used which optimises the result of a clustering process by x-means. The result of x-means is evaluated by using different quality measures. The optimisation methodology is illustrated within a case study, in which the selection of an appropriate set of features is a crucial part of the system. The intended application supports a user who is transcribing historical documents by showing him similar occurrences of a given glyph.

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Index Terms

Computer Science
Information Sciences

Keywords

Document Image Processing Genetic Algorithms Feature Selection Shape Descriptions Glyph Clustering X-Means