Offline Signature Identification Using Deep Learning and Euclidean Distance

  • Made Prastha Nugraha Universitas Udayana
  • Adi Nurhadiyatna Faculty of Electrical Engineering and Computing, University of Zagreb Zagreb, Croatia
  • Dewa Made Sri Arsa Department of Information Technology, Udayana University

Abstract

Hand signature is one of human characteristic that human have since birth, which can be used as identity recognition. A high accuracy signature recognition is needed to identify the right owner of signature. This study present signature identification using a combination method between Deep Learning and Euclidean Distance.  3 different signature datasets are used in this study which consist of SigComp2009, SigComp2011, and private dataset. Signature images preprocessed using binary image conversion, Region of Interest, and thinning. Several testing scenarios is applied to measure proposed method robustness, such as usage of various Pretrained Deep Learning, dataset augmentation, and dataset split ratio modifier. The best accuracy achieved is 99.44% with high precision rate.

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References

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Published
2021-08-16
How to Cite
NUGRAHA, Made Prastha; NURHADIYATNA, Adi; ARSA, Dewa Made Sri. Offline Signature Identification Using Deep Learning and Euclidean Distance. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, [S.l.], v. 12, n. 2, p. 102-111, aug. 2021. ISSN 2541-5832. Available at: <https://ojs.unud.ac.id/index.php/lontar/article/view/73362>. Date accessed: 16 sep. 2021. doi: https://doi.org/10.24843/LKJITI.2021.v12.i02.p04.