UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 7 Issue 5
May-2020
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2005303


Registration ID:
231612

Page Number

13-18

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Title

OFFLINE SIGNATURE FORGERY DETECTION USING CONVOLUTIONAL NEURAL NETWORK

Abstract

Handwritten Signature is considered as one of an integral part of security as it can be used for verification and authentication. Precision is not maintained every time a person does the signature, different parameters like signature strokes, length, pixel depth, continuity, etc may vary. Such Properties of the signature has to be checked before verification and authentication. So authenticating a fake signature becomes a challenging task. A Signature Capturing and Recognition System will take the image of the signature as an input and will train the image by extracting various features and will store it in the database then using Convolutional Neural Networks it will be compared with the original source signature and recognize whether it is the original signature. For feature extraction algorithms like Grayscale and Binarization are used. Once the image is captured, it will be converted into a black and white image and then processed. This system needs to be trained very well in order to have better results. Signatures samples will be fed into the system for identification tests in order to maintain high accuracy in the system. Feature extraction is an important stage where the features of each signature are captured using the CNN algorithm. The idea of this step is to identify each and every minor detail of a signature. Subsequently identifying the features and extracting them properly will lead to a better or more accurate verification. A centralized database of correct signatures of the customers will be available. This particular database can be used by a lot of systems that require customer information and signature information.

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"OFFLINE SIGNATURE FORGERY DETECTION USING CONVOLUTIONAL NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 5, page no.13-18, May-2020, Available :http://www.jetir.org/papers/JETIR2005303.pdf

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2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"OFFLINE SIGNATURE FORGERY DETECTION USING CONVOLUTIONAL NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 5, page no. pp13-18, May-2020, Available at : http://www.jetir.org/papers/JETIR2005303.pdf

Publication Details

Published Paper ID: JETIR2005303
Registration ID: 231612
Published In: Volume 7 | Issue 5 | Year May-2020
DOI (Digital Object Identifier):
Page No: 13-18
Country: pune, Maharastra, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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