Artificial Neural Network based Signature Recognition and Verification System

Simple item page
dc.contributor.authorPrabodith, N.P.C.
dc.contributor.authorWimaladharma, S.T.C.I.
dc.date.accessioned2022-02-24T08:25:10Z
dc.date.available2022-02-24T08:25:10Z
dc.date.issued2013
dc.description.abstractThe signature is an important biometric attribute of a person which can be used to authenticate the personal identity because of its uniqueness for each person. Now a day the personal signature has a significant value in day to day works. Because of its uniqueness there is a certain pattern which can be identified by extracting certain unique features. Though in present days signatures are using as the valid authentication mechanism, some peoples are trying to imitate another person’s signature to achieve some narrow goals. It is also common sight in Sri Lanka place like banks, government organizations, universities etc. Therefore, it is essential to introduce a high accuracy validation mechanism for personal authentication. The proposed system uses an efficient image processing and feature extraction methods as well as well-trained neural network system. Methodology Implementation of the system mainly based on two phases, Training phase and recognition phase. Several technologies, Programming languages and Libraries are used to design and implement the system. In the training phase there are several steps to be completed by the system before the training get started. In pre-processing activities, system is mainly focusing on background elimination, cropping (crop signature image according to the signature bounds), thresholding, thinning, and image width transformation (Abikoye et al., 2011). Feature extraction is one of the important parts of the system because powerful features directly affect to the accuracy of the final output. In here firstly, system will collect some global features such as pixel density, width to height ratio, maximum horizontal count and maximum vertical count. Then system will extract some unique points based on vertical splitting and horizontal splitting (Ashwiniet al., 2012). Those feature points are related with the image geometric centre point. After the feature extraction process is completed and then system normalizes all the features and added them to the input vector. Main purpose of the normalization is to convert values into an acceptable range for the neural network (range between 0 and 1). Then, the normalize data are used to train the Neural Network. In the Recognition phase, all the steps up to feature normalization are accomplished and those features are compared with trained Neural Network. Finally, the system will generate a unique value and which reveals the acceptance and rejection of the relevant signature.en_US
dc.identifier.urihttp://www.erepo.lib.uwu.ac.lk/handle/123456789/8438
dc.language.isoenen_US
dc.publisherUva Wellassa University of Sri Lankaen_US
dc.subjectComputer Scienceen_US
dc.subjectScience and Technologyen_US
dc.subjectTechnologyen_US
dc.subjectSystemen_US
dc.subjectAutomationen_US
dc.subjectNetworksen_US
dc.titleArtificial Neural Network based Signature Recognition and Verification Systemen_US
dc.title.alternativeResearch Symposium 2013en_US
dc.typeOtheren_US
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
19-CST-Artificial Neural Network based Signature Recognition and Verification System .pdf
Size:
327.09 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Research Symposium-2013