Signature Authentication using Deep Learning
S. Sangeetha1, S. Pothumani2, K. Anita Davamani3
1VS. Sangeetha, Department of CSE, Bharath Institute of Higher Education and Research, Chennai, Tamilnadu, India.
2S. Pothumani, Department of CSE, Bharath Institute of Higher Education and Research, Chennai, India.
3Ms. K. Anita Davamani, Department of CSE, Bharath Institute of Higher Education and Research, Chennai, India.
Manuscript received on 04 July 2019 | Revised Manuscript received on 17 July 2019 | Manuscript Published on 23 August 2019 | PP: 531-535 | Volume-8 Issue-9S3 August 2019 | Retrieval Number: I31030789S319/2019©BEIESP | DOI: 10.35940/ijitee.I3103.0789S319
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© 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: This paper depicts the strategy to confirm marks utilizing profound learning. The paper covers the different modules and the design required to accomplish the reason. Convolutional neural systems are actualized to parse marks and feed forward neural systems are executed to investigate the attributes of the mark. Angle plummet is utilized to address mistakes with a technique got back to spread. This paper discusses how the mark on the check is contrasted with the mark in the database and how a last verification score is given to the client.
Keywords: Machine learning, Deep learning, Neural networks, Convolution, Pooling, Activation function, ReLU, Gradient Descent, Training, Test, Validation.
Scope of the Article: Machine Learning