Offline Signature Verification Using Local Radon Transform and Support Vector Machines

摘要

In this paper, we propose a new method for signature verification using local Radon Transform. The proposed method uses Radon Transform locally as feature extractor and Support Vector Machine (SVM) as classifier. The main idea of our method is using Radon Transform locally for line segments detection and feature extraction, against using it globally. The advantages of the proposed method are robustness to noise, size invariance and shift invariance. Having used a dataset of 600 signatures from 20 Persian writers, and another dataset of 924 signatures from 22 English writers, our system achieves good results. The experimental results of our method are compared with two other methods. This comparison shows that our method has good performance for signature identification and verification in different cultures.