Offline Signature Recognition using Hidden Markov Model(HMM)
TLDR
This paper presents a recognition system for offline signatures using Discrete Cosine Transform (DCT) and Hidden Markov Model (HMM) and shows that successful signatures recognition rates of 99.2% is possible.Citations
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Journal ArticleDOI
The effective use of the one-class SVM classifier for handwritten signature verification based on writer-independent parameters
TL;DR: This paper proposes the use of One-Class Support Vector Machine (OC-SVM) based on writer-independent parameters, which takes into consideration only genuine signatures and when forgery signatures are lack as counterexamples for designing the HSVS.
Journal ArticleDOI
Discriminative Features Mining for Offline Handwritten Signature Verification
TL;DR: Combination of orientation of the skeleton and gravity centre point to extract accurate pattern features of signature data in offline signature verification system is presented.
Journal ArticleDOI
Biometric signature authentication using machine learning techniques: Current trends, challenges and opportunities
TL;DR: The techniques of offline and online signature verification systems according to the taxonomy of classification model are surveyed and the most notable challenges are presented to guide the readers towards the current trends and future directions of the domain.
Journal ArticleDOI
An appraisal of off-line signature verification techniques
TL;DR: In this paper, the authors have surveyed different papers on techniques that are currently used for the identification and verification of Offline signatures and they have presented a survey of signatures verification techniques in the field of biometrics.
Proceedings ArticleDOI
State-of-the-Art in Handwritten Signature Verification System
TL;DR: State-of-Art about both types of HSV Systems is presented, current methods used for features extraction and approaches used for verification in signature systems are presented.
References
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Journal ArticleDOI
A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Book
Fundamentals of speech recognition
TL;DR: This book presents a meta-modelling framework for speech recognition that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of manually modeling speech.
Journal ArticleDOI
Online and off-line handwriting recognition: a comprehensive survey
TL;DR: The nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms are described.
Journal ArticleDOI
Automatic signature verification: the state of the art—1989–1993
Franck Leclerc,Réjean Plamondon +1 more
TL;DR: This paper summarizes the activity from year 1989 to 1993 in automatic signature verification and reports on the different projects dealing with dynamic, static and neural network approaches.
Proceedings ArticleDOI
Off-line signature verification using HMM for random, simple and skilled forgeries
TL;DR: The experiments have shown that the error rates of the simple and random forgery signatures are very closed, and this reflects the real applications in which the simple forgeries represent the principal fraudulent case.