Persian off-line signature recognition with structural and rotation invariant features using by one-against-all SVM classifier

Subject Areas : B. Computer Systems Organization

Mohammad Mohammadzade ¹ , Alireza Ghonodi ²

1 - Computer Engineering Department, Sari Branch, Islamic Azad University, Sari, Iran
2 - Computer Engineering Department, Sari Branch, Islamic Azad University, Sari, Iran

Received: 2013-09-18 Accepted : 2013-09-18 Published : 2013-05-01

Keywords: Rotation invariant, structural feature, SVM, Persian off-line signature recognition,

Abstract :

The problem of automatic signature recognition has received little attention in comparison with the problem of signature verification, despite its potential applications for many business processes and can be used effectively in paperless office projects. This paper presents model-based off-line signature recognition with rotation invariant features. Non-linear rotation of signature patterns is one of the major difficulties to be solved in this problem. The proposed system is designed based on support vector machines (SVM) classifier technique and rotation invariant structure feature to tackle the problem. Our designed system consists of three stages: the first is preprocessing stage, the second is feature extraction stage and the last is SVM classifier stage. Experimental results demonstrated that the proposed methods were effective to improve recognition accuracy.