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Handwritten signature identification using basic concepts of graph theory

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TLDR
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.
Abstract
Handwritten signature is being used in various applications on daily basis. The problem arises when someone decides to imitate our signature and steal our identity. Therefore, there is a need for adequate protection of signatures and a need for systems that can, with a great degree of certainty, identify who is the signatory. This paper presents previous work in the field of signature and writer identification to show the historical development of the idea and defines a new promising approach in handwritten signature identification based on some basic concepts of graph theory. This principle can be implemented on both on-line handwritten signature recognition systems and off-line handwritten signature recognition systems. Using graph norm for fast classification (filtration of potential users), followed by comparison of each signature graph concepts value against values stored in database, the system reports 94.25% identification accuracy.

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Citations
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Combining graph edit distance and triplet networks for offline signature verification

TL;DR: Offline signature verification is a challenging pattern recognition task where a writer model is inferred using only a small number of genuine signatures using a combination of complementary writer mode and reader mode.
Book ChapterDOI

A Novel Graph Database for Handwritten Word Images

TL;DR: Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Patternrecognition (SSPR), and S+S SPR 2016: Structural, Syntactic, and Statistical pattern recognition.
Book ChapterDOI

Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks

TL;DR: This work proposes to complement a recent structural approach to offline signature verification based on graph edit distance with a statistical approach based on metric learning with deep neural networks, and demonstrates that combining the structural and statistical models leads to significant improvements in performance.
Proceedings ArticleDOI

A Structural Approach to Offline Signature Verification Using Graph Edit Distance

TL;DR: A novel structural approach to offline signature verification using an efficient cubic-time approximation of graph edit distance is introduced and several ways of creating, normalizing, and comparing signature graphs built from keypoints are put forward.
References
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Journal ArticleDOI

Writer independent on-line handwriting recognition using an HMM approach

TL;DR: A combination of signal normalization preprocessing and the use of invariant features makes the HMM based writer independent handwriting recognition system robust with respect to variability among di!erent writers as well as di?erent writing environments and ink collection mechanisms.
Journal ArticleDOI

A writer identification system for on-line whiteboard data

TL;DR: Different sets of features are extracted from the recorded data and used to train a text and language independent on-line writer identification system based on Gaussian mixture models (GMMs) which provide a powerful yet simple means of representing the distribution of the features extracting from the handwritten text.
Proceedings ArticleDOI

Offline Handwritten Signature Identification and Verification Using Contourlet Transform

TL;DR: A new method for signature identification and verification based on contourlet transform (CT) is proposed that is independency to nation of signers and achieves a 100% recognition rate and more than 96.5% error in verification.
Posted Content

Offline Signature Identification by Fusion of Multiple Classifiers using Statistical Learning Theory

TL;DR: This paper uses Support Vector Machines (SVM) to fuse multiple classifiers for an offline signature system and the results are found to be promising.
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