UNREADABLE OFFLINE HANDWRITING SIGNATURE VERIFICATION BASED ON GENERATIVE ADVERSARIAL NETWORK USING LIGHTWEIGHT DEEP LEARNING ARCHITECTURES

Jafar Majidpour, Fatih Özyurt, Mohammed Hussein Abdalla, Yu Ming Chu and Naif D. Alotaibi ()
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Jafar Majidpour: Department of Computer Science, University of Raparin, Rania, Iraq
Fatih Özyurt: ��Department of Software Engineering, Faculty of Engineering, Firat University, Elazig, Turkey
Mohammed Hussein Abdalla: Department of Computer Science, University of Raparin, Rania, Iraq
Yu Ming Chu: ��Institute for Advanced Study Honoring Chen Jian Gong, Hangzhou Normal University, Hangzhou 311121, P. R. China
Naif D. Alotaibi: �Communication Systems and Networks Research Group, Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia

FRACTALS (fractals), 2023, vol. 31, issue 06, 1-14

Abstract: Today, it is known that there are great difficulties and problems in signature and signature examinations, which have a very important place in both our private life and business and commercial life. The major issue arises when the manuscript’s signature is so illegible and unclear that it is difficult, if not impossible, to authenticate it with the human eye. Researchers have proposed traditional deep learning techniques to solve or improve this challenge. However, the results are not satisfactory. In this study, a new use of Generative Adversarial Network (GAN) model is proposed as a high-quality data synthesis method to address the unreadable data problem on signature verification. A unique signature verification method based on Lightweight deep learning architecture is also proposed. The suggested data synthesizing approach is evaluated using three frequently used Convolutional Neural Network (CNN) methods: MobileNet, SqueezeNet, and ShuffleNet. In addition, in preprocessing phase, we added three different types of high-intensity noise, including Salt & Pepper (S&P), Gaussian, and Gaussian Blur, to the images to make the signature unreadable. We utilized Indic scripts dataset to train GAN and CNN models in our approach. The great quality of images generated by GAN model, as well as the signature verification of the generated images, point to the suggested model’s strong performance.

Keywords: Noise; GAN; Lightweight Deep Learning Architecture; Synthesize Images; Signature Biometric (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0218348X23401011

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