Offline Signature Identification Using Deep Learning and Euclidean Distance

Made Prastha Nugraha; Adi Nurhadiyatna; Dewa Made Sri Arsa

doi:10.24843/LKJITI.2021.v12.i02.p04

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

DOI: https://doi.org/10.24843/LKJITI.2021.v12.i02.p04

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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