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Handwritten Signature Verification Using P-tree
A.K.M. Najmul Islam
Asian Journal of Information Technology , 2012,
Abstract: Handwritten signature recognition is a very complex task. At present many implementations are available for recognition of handwritten signatures. But none of them gives the hundred percent performances and their implementation is also very complex. This paper presents the implementation of handwritten signature verification system using the P-tree. The implementation of this system is very straightforward. This system takes a scanned image of the handwritten signature and tries to match it with already stored images in the database. This method uses the concept of P-tree. The signatures are converted into P-trees and then stored into the database. The signature, which needs to be verified, is also converted into a P-tree.
Handwritten Signature Verification using Instance Based Learning
Priya Metri , Ashwinder Kaur
International Journal of Computer Trends and Technology , 2011,
Abstract: 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 Correlation between 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 a mean value from the correlation value of one person signature then compute deviation from it which is used for verification.
A Survey of Dynamic Handwritten Signature Verification
动态手写签名验证技术概述

SHEN Feng YANG Fei YUAN Yu-Liang PAN Jin-Gui,
沈峰
,杨飞

计算机科学 , 2003,
Abstract: With the development of e-commerce, people are attaching more importance to biometrics authentication techniques. Dynamic handwritten signature verification is an effective one. This paper introduces the basic process of dynamic handwritten signature verification, some algorithms in use and performance evaluation.
Offline Handwritten Signature Verification - Literature Review  [PDF]
Luiz G. Hafemann,Robert Sabourin,Luiz S. Oliveira
Statistics , 2015,
Abstract: The area of Handwritten Signature Verification has been broadly researched in the last decades and still remains as an open research problem. This report focuses on offline signature verification, characterized by the usage of static (scanned) images of signatures, where the objective is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). We present an overview of how the problem has been handled by several researchers in the past few decades and the recent advancements in the field.
An off Line System for the Handwritten Numeric Chains Recognition
Salim Ouchtati,Mouldi Bedda,Faouzi Bouchareb,Abderrazak Lachouri
International Journal of Soft Computing , 2012,
Abstract: In this syudy we present an off line system for the recognition of the handwritten numeric chains. Our study is divided in two big parts. The first part is the realization of a recognition system of the isolated handwritten digits. In this study is based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm. The used parameters to form the input vector of the neural network are extracted on the binary images of the digits by two methods: the centred moments of the distributions sequences and the Barr features. The second part is the extension of our system for the reading of the handwritten numeric chains constituted of a variable number of digits. The vertical projection is used to segment the numeric chain at isolated digits and every digit (or segment) will be presented separately to the entry of the system achieved in the first part (recognition system of the isolated handwritten digits). The result of the recognition of the numeric chain will be displayed at the exit of the global system.
Handwritten Signature Verification Using Complementary Statistical Models  [cached]
Alan McCabe,Jarrod Trevathan
Journal of Computers , 2009, DOI: 10.4304/jcp.4.7.670-680
Abstract: This paper describes a system for performing handwritten signature verification using complementary statistical models. The system analyses both the static features of a signature (e.g., shape, slant, size), and its dynamic features (e.g., velocity, pen-tip pressure, timing) to form a judgment about the signer’s identity. This approach’s novelty lies in combining output from existing Neural Network and Hidden Markov Model based signature verification systems to improve the robustness of any specific approach used alone. The system performs reasonably well and achieves an overall error rate of 2:1% in the best case. The results of several other experiments are also presented including using less reference signatures, allowing multiple signing attempts, zero- effort forgery attempts, providing visual feedback, and signing a password rather than a signature.
The Design and Implementation of An On-line Handwritten Signature Verification
一个在线手写签名身份认证系统的设计与实现

杨飞,沈峰,袁余良,潘金贵
计算机科学 , 2002,
Abstract: With the development of computer network, Biometrics, as a kind of information security techniques, is thought more and more of. Handwritten signature verification is an effective one. This paper introduces the design and implementation of an on-line handwritten signature verification system which is based on dynamic programming matching algorithm and give a brief evaluation of its performance. The performance of the system is satisfying according to the experiment.
Neural Network-based Offline Handwritten Signature Verification System using Hu’s Moment Invariant Analysis
Sandeep Patil,Shailendra Dewangan
International Journal of Engineering and Advanced Technology , 2011,
Abstract: Handwritten signatures are considered as the mostnatural method of authenticating a person’s identity (comparedto other biometric and cryptographic forms of authentication).The learning process inherent in Neural Networks (NN) can beapplied to the process of verifying handwritten signatures thatare electronically captured via a stylus. This paper presents amethod for verifying handwritten signatures by using NNarchitecture. Various static (e.g., area covered, number ofelements, height, slant, etc.) [1] and dynamic (e.g., velocity, pentip pressure, etc.) signature features are extracted and used totrain the NN [2]. Several Network topologies are tested and theiraccuracy is compared.Although the verification process can be thought to as amonolith component, it is recommended to divide it into looselycoupled phases (like preprocessing, feature extraction, featurematching, feature comparison and classification) allowing us togain a better control over the precision of different components.This paper focuses on classification, the last phase in theprocess, covering some of the most important generalapproaches in the field. Each approach is evaluated forapplicability in signature verification, identifying their strengthand weaknesses. It is shown, that some of these weak points arecommon between the different approaches and can partially beeliminated with our proposed solutions. To demonstrate this,several local features are introduced and compared usingdifferent classification approaches.
Handwritten Devanagari Character Recognition using Artificial Neural Network
P.B. Khanale,S.D. Chitnis
Journal of Artificial Intelligence , 2011,
Abstract: The reading skills of computer are still way behind that of human beings. Most character recognition systems cannot read degraded documents and handwritten characters or words. Devanagari, an alphabetic script, is used by over 500 million people all over the world. In this study, we are presenting a Devanagari handwritten character recognition system using Artificial Neural Network. Up to 96% recognition rate is achieved for certain characters.
Use of Splines in Handwritten Character Recognition
Sunil Kumar,Gopinath S,,Satish Kumar,Rajesh Chhikara
International Journal on Computer Science and Engineering , 2010,
Abstract: Handwritten Character Recognition is software used to identify the handwritten characters and receive and interpret intelligible andwritten input from sources such as manuscript documents. The recent past several years has seen the development of many systems which are able to simulate the human brain actions. Among the many, the neural networks and the artificial intelligence are the most two important paradigms used. In this paper we propose a new algorithm for recognition of handwritten texts based on the spline function and neural network is proposed. In this approach the converse order of thehandwritten character structure task is used to recognize the character. The spline function and the steepest descent methodsare applied on the optimal notes to interpolate and approximatecharacter shape. The sampled data of the handwritten text are used to obtain these optimal notes. Each character model is constructed by training the sequence of optimal notes using the neural network. Lastly the unknown input character is compared by all characters models to get the similitude scores.
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