Offline Signature Verification Using the Discrete Radon Transform and a Hidden Markov Model
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Offline Signature Verification Using the Discrete Radon Transform and a Hidden Markov Model J. Coetzer Department of Applied Mathematics, University of Stellenbosch, Matieland 7602, South Africa Email: [email protected]
B. M. Herbst Department of Applied Mathematics, University of Stellenbosch, Matieland 7602, South Africa Email: [email protected]
J. A. du Preez Department of Electrical and Electronic Engineering, University of Stellenbosch, Matieland 7602, South Africa Email: [email protected] Received 31 October 2002; Revised 27 June 2003 We developed a system that automatically authenticates offline handwritten signatures using the discrete Radon transform (DRT) and a hidden Markov model (HMM). Given the robustness of our algorithm and the fact that only global features are considered, satisfactory results are obtained. Using a database of 924 signatures from 22 writers, our system achieves an equal error rate (EER) of 18% when only high-quality forgeries (skilled forgeries) are considered and an EER of 4.5% in the case of only casual forgeries. These signatures were originally captured offline. Using another database of 4800 signatures from 51 writers, our system achieves an EER of 12.2% when only skilled forgeries are considered. These signatures were originally captured online and then digitally converted into static signature images. These results compare well with the results of other algorithms that consider only global features. Keywords and phrases: offline signature verification, discrete Radon transform, hidden Markov model.
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INTRODUCTION
The purpose of our research is to develop a system that automatically classifies handwritten signature images as authentic or fraudulent, with as little misclassifications as possible. At the same time, the processing requirements must be feasible so as to make the adoption of such an automated system economically viable. Our work is inspired by, amongst others, the potential financial benefits that the automatic clearing of cheques will have for the banking industry. Despite an increasing number of electronic alternatives to paper cheques, fraud perpetrated at financial institutions in the United States has become a national epidemic. The National Check Fraud Center Report of 2000 [1] states that: “. . . cheque fraud and counterfeiting are among the fastest-growing crimes affecting the United States’ financial system, producing estimated annual losses exceeding $10 billion with the number continuing to rise at an alarming rate each year.” Since commercial banks pay little attention to verify-
ing signatures on cheques—mainly due to the number of cheques that are processed daily—a system capable of screening casual forgeries should already prove beneficial. In fact, most forged cheques contain forgeries of this type. We developed a system that automatically authenticates documents based on the owner’s handwritten signature. It should be noted that our system assumes that the signatures have already been extracted from the documents. Methods for extracting sign
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