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Authors: Ioana Bărbănţan ; Camelia Lemnaru and Rodica Potolea

Affiliation: Technical University of Cluj-Napoca, Romania

ISBN: 978-989-8425-05-8

Keyword(s): Signature Recognition, Hierarchical Classifier, Classification, Clustering, Naïve Bayes, Feature Selection, Learning Curve.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Bayesian Networks ; Biomedical Engineering ; Biomedical Signal Processing ; Business Analytics ; Computational Intelligence ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Datamining ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Health Information Systems ; Human-Computer Interaction ; Industrial Applications of Artificial Intelligence ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Knowledge Engineering ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Knowledge-Based Systems Applications ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Software Engineering ; Symbolic Systems ; Theory and Methods

Abstract: This paper presents an original approach for solving the problem of offline handwritten signature recognition, and a new hierarchical, data-partitioning based solution for the recognition module. Our approach tackles the problem we encountered with an earlier version of our system when we attempted to increase the number of classes in the dataset: as the complexity of the dataset increased, the recognition rate dropped unacceptably for the problem considered. The new approach employs a data partitioning strategy to generate smaller sub-problems, for which the induced classification model should attain better performance. Each sub-problem is then submitted to a learning method, to induce a classification model in a similar fashion with our initial approach. We have performed several experiments and analyzed the behavior of the system by increasing the number of instances, classes and data partitions. We continued using the Naïve Bayes classifier for generating the classification models for each data partition. Overall, the classifier performs in a hierarchical way: a top level for data partitioning via clustering and a bottom level for classification sub-model induction, via the Naïve Bayes classifier. Preliminary results indicate that this is a viable strategy for dealing with signature recognition problems having a large number of persons. (More)

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Paper citation in several formats:
Bărbănţan I.; Lemnaru C.; Potolea R. and (2010). A HIERARCHICAL HANDWRITTEN OFFLINE SIGNATURE RECOGNITION SYSTEM.In Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8425-05-8, pages 139-147. DOI: 10.5220/0002974001390147

@conference{iceis10,
author={Ioana Bărbănţan and Camelia Lemnaru and Rodica Potolea},
title={A HIERARCHICAL HANDWRITTEN OFFLINE SIGNATURE RECOGNITION SYSTEM},
booktitle={Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2010},
pages={139-147},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002974001390147},
isbn={978-989-8425-05-8},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - A HIERARCHICAL HANDWRITTEN OFFLINE SIGNATURE RECOGNITION SYSTEM
SN - 978-989-8425-05-8
AU - Bărbănţan, I.
AU - Lemnaru, C.
AU - Potolea, R.
PY - 2010
SP - 139
EP - 147
DO - 10.5220/0002974001390147

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