An Appraisal of Off-line Signature Verification Techniques (original) (raw)
Related papers
Handwritten signature verification is the process of confirming the identity of a user using the handwritten signature of the user as a form of behavioral biometrics for authentication and authorization in legal matter humans are recognized by their Signature. Every human being has their own writing style and hence their signature is used in the financial domain for identity verification. So it is necessary to develop a technique which is efficient in verifying the Handwritten Signature is correct or forge. This paper presents a technique of Handwritten Signature Verification based on offline method. Signatures are taken on A4 size paper then preprocessed, Handwritten Signature images using feature extracted from it. In this paper we have proposed a method to extract features from scanned image of signatures store it in database. We correlate features of all sample signatures for each person. Then we have to find that the given signature is genuine or forge.
Comparative Analysis of Offline Signature Verification System
A digital signature is a mathematical structure for indicating the validity of digital information or any document. A message is created by a known sender whose digital signature provides a recipient reason, such that the sender cannot reject having sent the message confirmation and that the message was not changed in transportation integrity. The Signature recognition and verification are a behavioral biometric. It can be operated in two various types: one is the Off-Line or Static Signature Verification Technique and another is the On-line or Dynamic Signature Verification Technique. In this paper, we are studying about Off-Line or Static Signature Verification Technique. In this method, users write their own signature on the blank paper and then digitize it with an optical scanner or a camera, and then the biometric system identifies the signature by analyzing its shape and this collection is also called as " off-line " Signature verification. Signature authentication can be divided into three main classes. These classes are based on how alike a forgery is in relation to signature and are identified as random, simple and skilled. In the random forgery the forger does not know about the signer's shape or signature name. In the simple forgery or unskillful forgery, the forger knows the name of the actual signer but don't know how his signature looks like. And in the skilled forgery, the forger knows both the information of the signer.
A Survey on Offline Signature Recognition and Verification Schemes
IOSR Journal of Electronics and Communication Engineering, 2012
Signature has been a distinguishing biometric feature through ages. They are extensively used as a means of personal verification; therefore an automatic verification system is needed. Even today thousands of financial and business transactions are being authorized via signatures. Signature verification finds its application in a large number of fields starting from online banking, passport verification systems to even authenticating candidates in public examinations from their signatures. This paper represents a brief survey on various off-line signature recognition & verification schemes.
OFFLINE AND ONLINE SIGNATURE VERIFICATION SYSTEMS A SURVEY
In recent years, due to the extraordinary diffusion of the internet in our daily life and simultaneously growing need of personal verification in many daily applications has driven signature verification system as an important aspect. This paper presents the survey about the offline & online signature verification system. Feature extraction stage is the most important stage in both online & off-line signature verification. The successful implementation of any such verification system depends mainly on how effectively the features have been extracted & used for classification. The robust features yield a successful verification system. Hence, in this paper, we are focusing on to put up with various feature extraction techniques & classifiers employed by various authors to attempt signature verification system. This paper summarizes different techniques used in signature verification system with their merits and demerits.
Handwritten Offline Signature Verification and Forgery Detection
Journal of emerging technologies and innovative research, 2021
Handwritten signatures are very useful in many applications. Such as in the field of bank-cheque processing, document authentication, ATM access, etc. Simultaneous signature verification and forgery detection are also important to avoid misuse and prove the identity of the person signing the document. In this paper, we discuss how the problem has been tackled over the past few decades and analysing the latest progress in this field.
Comparative Analysis of Off-line Signature Recognition
Biometrics (or biometric authentication) assigns to the confirmation of humans by their biological features. Computer science, biometrics to be specific, is used as an aspect of determination and access control. It is also used to determine individuals in groups that are under examination. Handwriting is one of the most widely used biometric systems for authentication of person as well as document. Online and offline signature is existing in person identification and authentication problems. Offline signature categorizes the signature into two classes: genuine and forged. In this paper, we discuss various features of offline signature recognition and verification process. We review and compare existing techniques, their results and methods of feature extraction.
AN INSPECTION ON OFFLINE SIGNATURE AUTHENTICATION
In the era of emergent technology, security is that the foremost anxiety to avoid replicas and counterfeits. There are diverse Biometric systems that enable in personal identification, amongst those verification systems, one system is Signature Verification System. Signatures are substantiated discrimination on-line and offline systems. Every human being has their own writing style and hence their signature is used in the financial domain for identity verification. So it is necessary to develop a technique which is efficient in verifying the Handwritten Signature is correct or forge. This paper describes the consequence of offline systems the survey of numerous methods related to offline signature verification systems.
Future Computing and Informatics Journal, 2020
Handwritten signature identification and verification has become an active area of research in recent years. Handwritten signature identification systems are used for identifying the user among all users enrolled in the system while handwritten signature verification systems are used for authenticating a user by comparing a specific signature with his signature that is stored in the system. This paper presents a review for commonly used methods for pre-processing, feature extraction and classification techniques in signature identification and verification systems, in addition to a comparison between the systems implemented in the literature for identification techniques and verification techniques in online and offline systems with taking into consideration the datasets used and results for each system
A SURVEY FOR OFFLINE SIGNATURE VERIFICATION AND RECOGNITION TECHNIQUES USING IMAGE PROCESSING
Signature Verification is the examination of a signature as it is widely used as a means of personal verification to discriminate between original and forged samples of a signatory. It offers two different types of schemes those are offline (static) and online (dynamic) verification techniques. Offline systems work on the scanned images while online systems use dynamic information of a signature captured at the time the signature is made. On comparing both offline systems found to be more complex due to the absence of stable dynamic characteristics and also due to highly stylish and unconventional writing styles despite that they are designed because of several benefits like systems does not requires signer's attendance as it is already stored in database. The aim is to lower down the false acceptance rate (FAR), false rejection rate (FRR) average error rate (AER) and equal error rate (EER). In this paper we reviewed and compared some popular existing verification techniques, their results and methods of feature extraction.