Proceedings ArticleDOI
Comparing the Effectiveness of Different Classifiers of Data Mining for Signature Recognition System
TLDR
This study tries to extract, investigate and compare the use of static and dynamic features using Five well-known classifiers such as AutoMLP, Naïve Bayes, Neural Network, Gradient Boosted Trees and Generalized Linear Model.Abstract:
Handwritten signatures are commonly used for the signification, authentication, and validation of people's important transactions and documents. However, this security measure could also be a threat at the same time. This study aims to develop a model to detect and recognize a signature. This study also tries to extract, investigate and compare the use of static and dynamic features using Five well-known (5) classifiers such as AutoMLP, Naive Bayes, Neural Network, Gradient Boosted Trees and Generalized Linear Model. The classifier that shows the most acceptable model is Neural Network which shows an accuracy rate of 92.88%.read more
Citations
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Journal ArticleDOI
Multilingual Handwritten Signature Recognition Based on High-Dimensional Feature Fusion
TL;DR: A handwritten signature recognition method that combines local maximum occurrence features (LOMO) and histogram of orientated gradients (HOG) features was proposed and achieved a recognition rate of 98.4% using a diverse signature database compared with existing methods.
Proceedings ArticleDOI
Multi-lingual Offline Signature Recognition Based on LOMO Feature
TL;DR: Wang et al. as mentioned in this paper proposed a multilingual hybrid handwritten signature recognition method based on local maximum occurrence features for minority languages like Uyghur and Chinese, which achieved the best recognition accuracy of 98.4% for the self-built signature dataset.
References
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Proceedings ArticleDOI
A systematic comparison between on-line and off-line methods for signature verification with hidden Markov models
Gerhard Rigoll,A. Kosmala +1 more
TL;DR: This paper may be the first systematic comparison of online and off-line methods for signature verification using exactly the same database, and leading to the surprising result that the difference in performance for both approaches is relatively small.
Proceedings ArticleDOI
Parameterization of a forgery handwritten signature verification system using SVM
TL;DR: A new method for off-line handwritten signature verification is described and it is compared with four different parameterization techniques using support vector machines (SVM) as a classification system to show which one is the most suitable parameterization technique.
Book ChapterDOI
Signature Verification Using Static and Dynamic Features
TL;DR: A signature verification algorithm based on static and dynamic features of online signature data is presented and combines the results obtained from three feature sets to attain an accuracy of 98.18%.
An Empirical Comparison of Kernel Selection for Support Vector Machines.
TL;DR: This paper examined the performance of SVMs and ANNs with different architectural and parameter settings for both binary and multi-class classification problems based on a vowel speech dataset and showed that SVM outperformed ANN greatly in multi- class classification problems.
Proceedings ArticleDOI
Online Signature Verification Based on Biometric Features
TL;DR: This paper tried to authenticate user automatically with electronic signatures on mobile device using four different classification algorithms to build a specific signature verification model for each user, and compared the verification accuracy of these algorithms.
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