This paper presents experiments with genetically
engineered feature sets for recognition of on-line
handwritten characters. These representations stem from
a nondescript decomposition of the character frame into
a set of rectangular regions, possibly overlapping,
each represented by a vector of 7 fuzzy variables.
Efficient new feature sets are automatically discovered
using genetic programming techniques. Recognition
experiments conducted on isolated digits of the Unipen
database yield improvements of more than 3percent over
a previously manually designed representation where
region positions and sizes were fixed.
%0 Conference Paper
%1 AleLem02
%A Lemieux, Alexandre
%A Gagné, Christian
%A Parizeau, Marc
%B Eighth International Workshop on Frontiers in
Handwriting Recognition 2002 (IWFHR 2002)
%C Niagara-on-the-Lake, Ontario, Canada
%D 2002
%K Unipen algorithms, base character classification, database, decomposition, extraction, feature floating frame fuzzy fuzzy-regional genetic handwriting handwritten operators, pattern programming, recognition, region regions, representation, representations, set sets, theory,
%P 145--150
%R doi:10.1109/IWFHR.2002.1030900
%T Genetical Engineering of Handwriting Representations
%U http://citeseer.ist.psu.edu/509026.html
%X This paper presents experiments with genetically
engineered feature sets for recognition of on-line
handwritten characters. These representations stem from
a nondescript decomposition of the character frame into
a set of rectangular regions, possibly overlapping,
each represented by a vector of 7 fuzzy variables.
Efficient new feature sets are automatically discovered
using genetic programming techniques. Recognition
experiments conducted on isolated digits of the Unipen
database yield improvements of more than 3percent over
a previously manually designed representation where
region positions and sizes were fixed.
@inproceedings{AleLem02,
abstract = {This paper presents experiments with genetically
engineered feature sets for recognition of on-line
handwritten characters. These representations stem from
a nondescript decomposition of the character frame into
a set of rectangular regions, possibly overlapping,
each represented by a vector of 7 fuzzy variables.
Efficient new feature sets are automatically discovered
using genetic programming techniques. Recognition
experiments conducted on isolated digits of the Unipen
database yield improvements of more than 3percent over
a previously manually designed representation where
region positions and sizes were fixed.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Niagara-on-the-Lake, Ontario, Canada},
author = {Lemieux, Alexandre and Gagn\'e, Christian and Parizeau, Marc},
biburl = {https://www.bibsonomy.org/bibtex/2e07a1d98168bd23a39b940b8cf608e01/brazovayeye},
booktitle = {Eighth International Workshop on Frontiers in
Handwriting Recognition 2002 (IWFHR 2002)},
doi = {doi:10.1109/IWFHR.2002.1030900},
interhash = {25b8d3b1464d39bdb2261b19af879028},
intrahash = {e07a1d98168bd23a39b940b8cf608e01},
keywords = {Unipen algorithms, base character classification, database, decomposition, extraction, feature floating frame fuzzy fuzzy-regional genetic handwriting handwritten operators, pattern programming, recognition, region regions, representation, representations, set sets, theory,},
month = {August 6-8},
pages = {145--150},
timestamp = {2008-06-19T17:45:21.000+0200},
title = {Genetical Engineering of Handwriting Representations},
url = {http://citeseer.ist.psu.edu/509026.html},
year = 2002
}