Arabic Handwritten Signature Identification

Taqa, Alaa Y.; Mahmood, Hanaa F.

doi:10.33899/csmj.2013.163525

	Arabic Handwritten Signature Identification
AL-Rafidain Journal of Computer Sciences and Mathematics
Article 4, Volume 10, Issue 3, 2013, Pages 37-54 PDF (563.96 K)
Document Type: Research Paper
DOI: 10.33899/csmj.2013.163525
Authors
Alaa Y. Taqa; Hanaa F. Mahmood
College of Education University of Mosul, Mosul, Iraq
Abstract
This paper proposes a new intelligent off-line Arabic handwritten signature identification and verification system based on texture analysis. The system uses the texture as feature and back propagation neural network as classifier. The signature image is preprocessed by several operations (Noise removal, Conversion of the signature image to binary image, Finding outer rectangle, Thinning and Size normalization) then the fractal number and co-occurrence matrix are computed to estimate texture features. In this work, two off-line Arabic handwritten signature identification systems are constructed. The first one uses the nearest Euclidean distance, while the other uses back propagation neural network. The paper analyzes and compares the results obtained from the two proposed systems to show the robustness level of the proposed intelligence system. Furthermore, the proposed system was tested by using Genuine signatures and has achieved a CCR (Correct Classification Rate) of 100% in best cases, while it was tested by using Forged signatures it has achieved a CRR approximated to 96.3% in best cases. The experimental results showed that the proposed system is efficient and competent with other state-of-the-art texture-based off-line signature identification systems.
Keywords
Texture analysis; Back propagation artificial neural network; Offline signature identification and verification

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