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Optimized Signature Recognition System Based on Feature Extraction Methods and Neural Network Classifier

Mohammed A. Abdala*, Saja K. Jamee**

*Associate Professor, Department of Information Engineering, University of Nahrain, Baghdad, Iraq.

**M.Sc. Degree in Information Engineering, Department of Information Engineering, University of Nahrain, Baghdad, Iraq.

Periodicity:October - December'2008
DOI : https://doi.org/10.26634/jse.3.2.245

Abstract

This paper presents new techniques for off-line signature recognition. The proposed systems are based on invariant moment, texture and global features. For each one of these features sets neural network classifiers which are based on MLP (Multilayer Perceptron), RBF (Radial Basis Function) and Elman neural network classifier are implemented. These nine signature recognition systems are tested then the optimized system that has the highest recognition rate is selected. The optimized system is evaluated with rotation and scaling effects.

Keywords

Signature recognition, Global, Texture, Moment, Neural classifiers, MLP, RBF, Elman, Optimized system

How to Cite this Article?

Mohammed A. Abdala and Saja K. Jameel (2008). Optimized Signature Recognition System Based on Feature Extraction Methods and Neural Network Classifier. i-manager’s Journal on Software Engineering, 3(2),20-27. https://doi.org/10.26634/jse.3.2.245

References

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