TY - GEN
T1 - Building a Model of Signature Verification and Identification System Based on Deep CNN (ConvNet)
AU - Shirisha, R.
AU - Divya, P.
AU - Aiswarya, V. B.
AU - Yoshitha, S.
AU - Sadique, Jasim
AU - Premkumar, Anitha
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - Signatures are commonly used as a unique way of identifying and verifying a person’s identity. Legal documents like bank cheques and transactions, government documents, and affidavits require signature-based verification to identify the authenticity of the person. The signature identification and verification system help to distinguish whether an input signature is honest or a fake. For very long years, this was considered to be a challenging mission when the offline mode was taken into consideration. The offline mode uses scanned signature images, where the online mode of signing process isn't existing. To overcome the signature verification challenge, an application of deep learning techniques to identify feature representations from signature images can be leveraged. In this paper, we have demonstrated how to identify valid and invalid signatures by building a signature verification and identification system that uses deep convolution neural network (ConvNet).
AB - Signatures are commonly used as a unique way of identifying and verifying a person’s identity. Legal documents like bank cheques and transactions, government documents, and affidavits require signature-based verification to identify the authenticity of the person. The signature identification and verification system help to distinguish whether an input signature is honest or a fake. For very long years, this was considered to be a challenging mission when the offline mode was taken into consideration. The offline mode uses scanned signature images, where the online mode of signing process isn't existing. To overcome the signature verification challenge, an application of deep learning techniques to identify feature representations from signature images can be leveraged. In this paper, we have demonstrated how to identify valid and invalid signatures by building a signature verification and identification system that uses deep convolution neural network (ConvNet).
UR - https://www.scopus.com/pages/publications/85123318254
UR - https://www.scopus.com/pages/publications/85123318254#tab=citedBy
U2 - 10.1007/978-981-16-5987-4_66
DO - 10.1007/978-981-16-5987-4_66
M3 - Conference contribution
AN - SCOPUS:85123318254
SN - 9789811659867
T3 - Lecture Notes in Networks and Systems
SP - 649
EP - 657
BT - ICT Systems and Sustainability - Proceedings of ICT4SD 2021
A2 - Tuba, Milan
A2 - Akashe, Shyam
A2 - Joshi, Amit
PB - Springer Science and Business Media Deutschland GmbH
T2 - 6th International Conference on ICT for Sustainable Development, ICT4SD 2021
Y2 - 5 August 2021 through 6 August 2021
ER -