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Using Neural Network to Identify Forgery in Offline Signatures

  • Piyush Agrawal
  • Rahul Bhalsodia
  • Yash GargEmail author
Conference paper
  • 29 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1087)

Abstract

This paper proposes an efficient method for the identification of forged signatures from offline handmade signatures. Signatures are majorly used as personal verification that can be abused by any unauthorized third party who would feign the identification of an individual which signifies the need for an automatic forgery identification system. Over a past few decades, there are significant attempts for the identification of forgery of signatures. Identification can be done either online- or offline-based mode. Offline mode works on the input image of a sign. We aim to purpose a technique for offline identification by using a simple shape based on geometrical features including area, center of gravity, eccentricity, kurtosis. Database of signatures is trained for two classes, genuine and forged. Two different classifiers, one based on SVM and other on artificial neural network (ANN), were used to identify and classify the signatures as genuine and forged.

Keywords

Offline signature forgery Deep neural network Machine learning 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Bharati Vidyapeeth’s College of EngineeringNew DelhiIndia

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