3,618 research outputs found
Handwritten Signature Verification using Deep Learning
Every person has his/her own unique signature that is used mainly for the purposes of personal
identification and verification of important documents or legal transactions. There are two kinds of signature
verification: static and dynamic. Static(off-line) verification is the process of verifying an electronic or document
signature after it has been made, while dynamic(on-line) verification takes place as a person creates his/her
signature on a digital tablet or a similar device. Offline signature verification is not efficient and slow for a large
number of documents. To overcome the drawbacks of offline signature verification, we have seen a growth in
online biometric personal verification such as fingerprints, eye scan etc. In this paper we created CNN model
using python for offline signature and after training and validating, the accuracy of testing was 99.70%
Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks
Biometric authentication by means of handwritten signatures is a challenging
pattern recognition task, which aims to infer a writer model from only a
handful of genuine signatures. In order to make it more difficult for a forger
to attack the verification system, a promising strategy is to combine different
writer models. In this work, we propose to complement a recent structural
approach to offline signature verification based on graph edit distance with a
statistical approach based on metric learning with deep neural networks. On the
MCYT and GPDS benchmark datasets, we demonstrate that combining the structural
and statistical models leads to significant improvements in performance,
profiting from their complementary properties
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