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General Information
    • ISSN: 2010-3719 (Online)
    • Abbreviated Title: Int. J. Inf. Electron. Eng.
    • Frequency: Quarterly
    • DOI: 10.18178/IJIEE
    • Editor-in-Chief: Prof. Chandratilak De Silva Liyanage
    • Executive Editor: Jennifer Zeng
    • Abstracting/ Indexing : Google Scholar, Electronic Journals Library, Crossref and ProQuest, Ei (INSPEC, IET), EBSCO.
    • E-mail ijiee@ejournal.net
Editor-in-chief

 
University of Brunei Darussalam, Brunei Darussalam   
" It is a great honor to serve as the editor-in-chief of IJIEE. I'll work together with the editorial team. Hopefully, The value of IJIEE will be well recognized among the readers in the related field."

IJIEE 2019 Vol.9(4): 72-78 ISSN: 2010-3719
DOI: 10.18178/IJIEE.2019.9.4.709

Deep Learning Technique for Improving the Recognition of Handwritten Signature

Anusara Hirunyawanakul, Supaporn Bunrit, Nittaya Kerdprasop, and Kittisak Kerdprasop
Abstract—Handwritten signature recognition is a biometric task used extensively in our daily life. The efficacy of such system is important and challenging in that the recognition accuracy still has room for improvement. In this paper, we propose the use of Deep Convolutional Neural Networks (DCNN), which is a deep learning technique, to improve accuracy of handwritten signature recognition. We apply DCNN in two difference strategies for signature recognition: 1) transfer learning using leveraged features from a pre-trained model on a larger dataset, and 2) create CNN model from scratch. Our studied dataset consists of 600 pictures of handwritten signatures collected from 30 people. In order to evaluate the effectiveness of the proposed method, the accuracy is compared with the results obtained from various machine learning methods. The comparison reveals very satisfied recognition results in the sense that the two proposed strategies achieve 100% of the recognition rate. To compare the two strategies in terms of training time, the strategy of creating DCNN model from scratch shows much lower training time than the transfer learning strategy.

Index Terms—Deep learning, deep convolutional neural networks, handwritten signature recognition, transfer learning.

A. Hirunyawanakul, S. Bunrit, and K. Kerdprasop are with the School of Computer Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand (e-mail: Anusara.hi@gmail.com, nittaya@sut.ac.th).
N. Kerdprasop is with the School of Computer Engineering and Data and Knowledge Engineering Research Unit, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand (e-mail: kerdpras@sut.ac.th).

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Cite:Anusara Hirunyawanakul, Supaporn Bunrit, Nittaya Kerdprasop, and Kittisak Kerdprasop, "Deep Learning Technique for Improving the Recognition of Handwritten Signature," International Journal of Information and Electronics Engineering vol. 9, no. 4, pp. 72-78, 2019.

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