Digital Signature Verification Using Artificial Neural Networks
Gopichand G1, Sailaja G2, N. VenkataVinod Kumar3, T. Samatha4
1Gopichand G, Assistant Professor, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
2Sailaja G, Assistant Professor, Department of Computer Science and Engineering, SV Engineering College for Women, Tirupati (Andhra Pradesh), India.
3N. Venkata Vinod Kumar, Assistant Professor, Department of Computer Science and Engineering, Annamacharya Institute of Technology & Sciences, Tirupati (Andhra Pradesh), India.
4T. Samatha, Assistant Professor, Department of Computer Science and Engineering, Annamacharya Institute of Technology & Sciences, Tirupati (Andhra Pradesh), India.
Manuscript received on 08 February 2019 | Revised Manuscript received on 21 February 2019 | Manuscript Published on 04 March 2019 | PP: 452-457 | Volume-7 Issue-5S2 January 2019 | Retrieval Number: ES2082017519/19©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Identification and verification of hard written signature from images is major issue. This is very difficult as even human eye does not have that much visual ability to identify every detail of the in handwritten. Signature changes every time so it is difficult for humans to identify the original and forged ones. By using deep learning which uses the sophisticated is digital configured replica of human brain, we can identify the forgery done in signature with higher accuracy.
Keywords: Deep Learning, Digital Configured Replica, Forgery, Signature.
Scope of the Article: Digital System and Logic Design