Dynamic zoning selection for handwritten character recognition

Authors:
Luciane Y. Hirabara;Simone B. K. Aires;Cinthia O. A. Freitas;Alceu S. Britto;Robert Sabourin
Affiliations:
PUCPR-Pontificia Universidade Católica do Paraná, Brazil;PUCPR-Pontificia Universidade Católica do Paraná, Brazil;PUCPR-Pontificia Universidade Católica do Paraná, Brazil;PUCPR-Pontificia Universidade Católica do Paraná, Brazil;ETS-Ecole de Technologie Supérieure, Canada
Venue:
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Year:
2011

Citing 4
Cited 1

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Large vocabulary off-line handwritten word recognition
Intelligent Zoning Design Using Multi-Objective Evolutionary Algorithms

ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Foreground and Background Information in an HMM-Based Method for Recognition of Isolated Characters and Numeral Strings

IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Evaluating a zoning mechanism and class-modular architecture for handwritten characters recognition

CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications

From static to dynamic ensemble of classifiers selection: Application to Arabic handwritten recognition

International Journal of Knowledge-based and Intelligent Engineering Systems

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Abstract

This paper presents a two-level based character recognition method in which a dynamically selection of the most promising zoning scheme for feature extraction allows us to obtain interesting results for character recognition. The first level consists of a conventional neural network and a look-up-table that is used to suggest the best zoning scheme for a given unknown character. The information provided by the first level drives the second level in the selection of the appropriate feature extraction method and the corresponding class-modular neural network. The experimental protocol has shown significant recognition rates for handwritten characters (from 80.82% to 88.13%).