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Offline Signature Verification

EasyChair Preprint no. 2538

4 pagesDate: February 5, 2020

Abstract

Now a day’s signature is becomes a most important biometric authentication technique. In banks or at the other necessary documents, signature plays an important role to authenticate the person. In this technique, we are going to present a deep learning approach for offline signature verification to prevent the fraud signatures by fake peoples. We are going to do deep learning with the help of Convolution Neural Network (CNN). In this study, we are going to collect dataset of different signatures from the different angles. Signature is taken as an input in the form of image. For signature recognition, it is important to make structural and some geometrical calculation getting to extract special features from the signatures then we train a man-made neural network on these features from different signers. Finally, the extracted features from the tested signature are compared with the previously trained features and that we know the signer.

Keyphrases: Biometric, CNN, deep learning, neural network, Signer

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:2538,
  author = {Shailesh Kad and Manisha Darak},
  title = {Offline Signature Verification},
  howpublished = {EasyChair Preprint no. 2538},

  year = {EasyChair, 2020}}
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