NDLI: Signature recognition using fuzzy min-max neural network

Content Provider	IEEE Xplore Digital Library
Author	Chaudhari, B.M. Barhate, A.A. Bhole, A.A.
Copyright Year	2009
Description	Author affiliation: Electrical Engg Dept Govt. College of Engg Aurangaba, India (Bhole, A.A.) \|\| E&Tc Engg., Dept. Godavari C.O.E. Jalgaon, India (Chaudhari, B.M.; Barhate, A.A.)
Abstract	The signature recognition system has been inspired from the human capability to recognize any pattern, which is a very difficult task for normal computer system having computing power of more then billions instructions per second. In this system the input is given in the form of a digital image by using writing pad, optical scanner, or Digital camera. This input image is processed to extract the information by using data acquisition and HU's seven moment invariant [21] to the order of three. Then the fuzzy min-max algorithm can applied to classify the signature pattern and this fuzzy min-max algorithm [1] is totally fit to the neural network framework. The neural network middle layer is work as fuzzified neuron and because of this the output can be correctly classified .Use of fuzzy membership function is increase the accuracy of the classification of signature pattern because the decision boundaries are not crisp rather it is fuzzy. The neural network is designed for this work is for the category learning which can increase the speed of recognition because in this fuzzy min-max neural network [1] the supervised learning algorithm is used also it can learn nonlinear class boundaries in a single pass through the data and provides the ability to incorporate new and refine existing classes without retraining. The advantage of our system is its accuracy in recognizing signature is nearly 53% for single signature pattern per class and if the signature pattern per class are increased then the accuracy is increased up to 92%.because patterns per class with slighter changes in it can increase the recognition efficiency naturally and there is no increase in training time because the neural network used is for category learning in which the dataset & index of the class is used for training.
Starting Page	1
Ending Page	7
File Size	328621
Page Count	7
File Format	PDF
ISBN	9781424447893
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-04
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Kongu Eng College
Subject Keyword	Digital images Humans Optical computing Digital cameras Pattern recognition classification Computer aided instruction Signature verification moment invariant Neural networks neural network for category learning fuzzy min-max neural network Writing Fuzzy neural networks Nonlinear optics
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	ndl-support@iitkgp.ac.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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