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Offline Signature Identification by Fusion of Multiple Classifiers using Statistical Learning Theory
TLDR
This paper uses Support Vector Machines (SVM) to fuse multiple classifiers for an offline signature system and the results are found to be promising.Abstract:
This paper uses Support Vector Machines (SVM) to fuse multiple classifiers for an offline signature system. From the signature images, global and local features are extracted and the signatures are verified with the help of Gaussian empirical rule, Euclidean and Mahalanobis distance based classifiers. SVM is used to fuse matching scores of these matchers. Finally, recognition of query signatures is done by comparing it with all signatures of the database. The proposed system is tested on a signature database contains 5400 offline signatures of 600 individuals and the results are found to be promising.read more
Citations
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Journal Article
Handwritten signature identification using basic concepts of graph theory
TL;DR: Previous work in the field of signature and writer identification is presented to show the historical development of the idea and a new promising approach in handwritten signature identification based on some basic concepts of graph theory is defined.
Book ChapterDOI
Hybrid Rough Neural Network Model for Signature Recognition
TL;DR: This chapter introduces an offline signature recognition technique using rough neural network and rough set, a new hybrid technique that achieves good results, since the short rough Neural Network algorithm is neglected by the grid features technique, and then the advantages of both techniques are integrated.
Journal ArticleDOI
Offline Handwritten Signature Identification Using Adaptive Window Positioning Techniques
TL;DR: Adaptive Window Positioning technique which focuses on not just the meaning of the handwritten signature but also on the individuality of the writer is proposed which can be used to detect signatures signed under emotional duress.
Proceedings ArticleDOI
Offline signature verification using grid based feature extraction
Swati Srivastava,Suneeta Agarwal +1 more
TL;DR: The proposed technique is based on the grid features extraction and deals with skilled forgeries and has been tested on two databases: Database A and a standard Database B (Set 1 and Set 2).
Proceedings ArticleDOI
Offline Signature Recognition System Using Radon Transform
TL;DR: A novel approach for off-line signature recognition system is presented in this work, which is based on local radon features, where totally 16 radon transform based projection features are extracted which are used to distinguish the different signatures.
References
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Proceedings ArticleDOI
Off-line signature verification using HMM for random, simple and skilled forgeries
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Journal ArticleDOI
Signature verification using multiple neural classifiers
Reena Bajaj,Santanu Chaudhury +1 more
TL;DR: Experimental results show that combination of the classifiers increases reliability of the recognition results and is the unique feature of this work.
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