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Offline Handwritten Signature Recognition using Artificial Neural Network Techniques

Published on October 2015 by Priyanka Narkhede, and V. R. Ingale
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International Conference on Advancements in Engineering and Technology (ICAET 2015)
Foundation of Computer Science USA
ICQUEST2015 - Number 1
October 2015
Authors: Priyanka Narkhede, and V. R. Ingale
ff2edd78-58cd-4b58-8d1c-2db0a44f75d8

Priyanka Narkhede, and V. R. Ingale . Offline Handwritten Signature Recognition using Artificial Neural Network Techniques. International Conference on Advancements in Engineering and Technology (ICAET 2015). ICQUEST2015, 1 (October 2015), 21-24.

@article{
author = { Priyanka Narkhede, and V. R. Ingale },
title = { Offline Handwritten Signature Recognition using Artificial Neural Network Techniques },
journal = { International Conference on Advancements in Engineering and Technology (ICAET 2015) },
issue_date = { October 2015 },
volume = { ICQUEST2015 },
number = { 1 },
month = { October },
year = { 2015 },
issn = 0975-8887,
pages = { 21-24 },
numpages = 4,
url = { /proceedings/icquest2015/number1/22980-2781/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Advancements in Engineering and Technology (ICAET 2015)
%A Priyanka Narkhede
%A and V. R. Ingale
%T Offline Handwritten Signature Recognition using Artificial Neural Network Techniques
%J International Conference on Advancements in Engineering and Technology (ICAET 2015)
%@ 0975-8887
%V ICQUEST2015
%N 1
%P 21-24
%D 2015
%I International Journal of Computer Applications
Abstract

Every person has its own signature, different from others. People make their signature in different manner which depending upon the type of pen available, space available or accomplish it with different angle. Exactly the same signature is not possible consistently. However, in some applications, itis very important to recognize the accurate signature where, the evaluation depends on theaccuracy and time. This paper presents a design of offline Signature recognition system using neural networks. A database is created by applying global and morphological operations on the signature using different ranges. Database of total 1344 signatures of 7 persons are used for experimentation. Daubechies wavelet transform employed to extract a set of features which areutilizes as input to neural network. A Feed forward back Propagation neural network and a Radial Basis Function (RBF) network are used for the examination. An accuracy of 97. 61 % with Feedforward back Propagation is observed in identifying the test signatures under different dilated and erodedimages of the signature.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Offline Signature Feature Extraction Daubechies Wavelet Morphology radial Basis Network Feedforward Back Propagation Network.