Genetical Engineering of Handwriting Representations

Authors:
Alexandre Lemieux;Christian Gagné;Marc Parizeau
Affiliations:
-;-;-
Venue:
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Year:
2002

Citing 0
Cited 4

Methods, Report and Survey for the Comparison of Diverse Isolated Character Recognition Results on the UNIPEN Database

ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Intelligent Zoning Design Using Multi-Objective Evolutionary Algorithms

ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
HBF49 feature set: A first unified baseline for online symbol recognition

Pattern Recognition
Zoning methods for handwritten character recognition: A survey

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

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 3% over a previously manually designed representation where region positions and sizes were fixed.