Title : Signature Verification Using Convolutional Neural Network

Authors : Shayekh Mohiuddin Ahmed Navid,Shamima Haque Priya , Nabiul Hoque Khandakar,Zannatul Ferdous , AKM Bahalul Haque

Abstract : Signatures are widely used to validate the authentication of an individual. A robust method is still awaited that can correctly certify the authenticity of a signature. The proposed solution provided in this paper is going to help individuals to distinguish signatures for determining whether a signature is forged or genuine. In our system, we aimed to automate the process of signature verification using Convolutional Neural Networks. Our model is constructed on top of a pre-trained Convolutional Neural Network called the VGG-19. We evaluated our model on widely accredited signature datasets with a multitude of genuine signature samples sourced from ICDAR[3], CEDAR[1] and Kaggle[2]; achieving accuracies of 100%, 88%, and 94.44% respectively. Our analysis shows that our proposed model can classify the signature if they do not closely resemble the genuine signature.

Journal : Volume : Year : 2019 Issue :
Pages : City : Dhaka Edition : Editors :
Publisher : IEEE ISBN : Book : Chapter :
Proceeding Title : 2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON) Institution : Issuer : Number :