An offline signature recognition and verification system based on neural network
Sameera Khan,Avinash Dhole +1 more
TLDR
In this system signature database of signature images is created, followed by image preprocessing, feature extraction, neural network design and training, and classification of signature as genuine or counterfeit.Abstract:
Various techniques are already introduced for personal identification and verification based on different types of biometrics which can be physiological or behavioral. Signatures lies in the category of behavioral biometric which can distort or changed with course of time. Signatures are considered to be most promising authentication method in all legal and financial documents. It is necessary to verify signers and their respective signatures. This paper presents an Offline Signature recognition and verification system(SRVS). In this system signature database of signature images is created, followed by image preprocessing, feature extraction, neural network design and training, and classification of signature as genuine or counterfeit.read more
Citations
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Journal ArticleDOI
Signature identification and verification techniques: state-of-the-art work
Harmandeep Kaur,Munish Kumar +1 more
TL;DR: An extensive systematic overview of online and offline signature identification and verification techniques in offline signature verification, surveys related to two approaches, namely, writer-dependent, and writer-independent approaches are presented.
Journal ArticleDOI
Automated Offline Arabic Signature Verification System using Multiple Features Fusion for Forensic Applications
TL;DR: It is concluded from the experimental results that the verification system performs well and has the ability to reduce both False Acceptance Rate (FAR) and False Rejection Rate (FRR).
Journal ArticleDOI
Distance and Fuzzy Classifiers Alliance: The Solution to Off-line Arabic Signature Verification System for Forensic Science
Saad M. Darwish,Zainab H. Noori +1 more
TL;DR: It is concluded from the experimental results that the verification system performs well and has the ability to reduce both False Acceptance Rate (FAR) and False Rejection Rate (FRR).
Journal ArticleDOI
Offline handwritten signature verification using various Machine Learning Algorithms
TL;DR: This paper introduces a novel approach to verify the signatures using difference of gaussian filtering technique, gray level co-occurrence matrix feature extraction technique, principle component analysis and kernel principal component analysis associated with various machine learning algorithms.
Journal ArticleDOI
Automatic Personality Analysis through Signatures
References
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Journal ArticleDOI
Offline geometric parameters for automatic signature verification using fixed-point arithmetic
TL;DR: A set of geometric signature features for offline automatic signature verification based on the description of the signature envelope and the interior stroke distribution in polar and Cartesian coordinates are presented.
Proceedings ArticleDOI
Global Features for the Off-Line Signature Verification Problem
TL;DR: Global features based on the boundary of a signature and its projections are described, and the combination of these features with the Modified Direction Feature (MDF) and the ratio feature showed promising results for the off-line signature verification problem.
Journal ArticleDOI
Handwritten Signature Verification using Neural Network
Ashwini Pansare,Shalini Bhatia +1 more
TL;DR: The method presented in this paper consists of image prepossessing, geometric feature extraction, neural network training with extracted features and verification, which includes applying the extracted features of test signature to a trained neural network which will classify it as a genuine or forged.
Off-line Signature Verification Based on Fusion of Grid and Global Features Using Neural Networks
TL;DR: Off-line Signature Verification Based on Fusion of Grid and Global Features Using Neural Networks (SVFGNN) is presented, where the global and grid features are fused to generate set of features for the verification of signature.
Offline Signature Recognition & Verification using Neural Network
TL;DR: Off-line signature recognition & verification using neural network is proposed, where the signature is captured and presented to the user in an image format and has been tested and found suitable for its purpose.