Offline Signature Verification Via Structural Methods: Graph Edit Distance and Inkball Models

Maergner, Paul; Howe, Nicholas R.; Riesen, Kaspar; Ingold, Rolf; Fischer, Andreas

doi:10.1109/icfhr-2018.2018.00037

Cited by 17 publications

(7 citation statements)

References 26 publications

(31 reference statements)

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“…To overcome the complexity of SVM, an addition of decision tree function produced better results previously, the hybrid SVM model was proposed embed C4.5 algorithm of a decision tree into the SVM and resulting in a more accurate and efficient hybrid classifier. Then we introduced the modified DT-SVM (Maergner et al, 2019;Blankers et al, 2009) algorithm addition of a new method, Probability-based Distance as Spitting Criterion, in which we use the distances in the frequency distribution of the instances. Thus, the modified DT-SVM provides better performance over the previous decision tree, and SVM (Ferrer et al, 2005) in comparison with the Computational Complexity and overall Accuracy.…”

Section: Proposed Workmentioning

confidence: 99%

An integrated approach on verification of signatures using multiple classifiers (SVM and Decision Tree): A multi-classification approach

al.¹

2022

Int. j. adv. appl. sci.

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A signature is a handwritten representation that is commonly used to validate and recognize the writer individually. An automated verification system is mandatory to verify the identity. The signature essentially displays a variety of dynamics and the static characteristics differ with time and place. Many scientists have already found different algorithms to boost the signature verification system function extraction point. The paper is aimed at multiplying two different ways to solve the problem in digital, manual, or some other means of verifying signatures. The various characteristics of the signature were found through the most adequately implemented methods of machine learning (support vector and decision tree). In addition, the characteristics were listed after measuring the effects. An experiment was performed in various language databases. More precision was obtained from the feature.

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Section: Proposed Workmentioning

confidence: 99%

An integrated approach on verification of signatures using multiple classifiers (SVM and Decision Tree): A multi-classification approach

al.¹

2022

Int. j. adv. appl. sci.

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“…Graph matching is a common technique in pattern recognition [8], [9], which has been applied in document analysis to keyword spotting [4] and signature verification [10]. The main idea is to define a graph, which traces the lines of a word or a signature with its edges, as shown in Fig.…”

Section: B Graph Matchingmentioning

confidence: 99%

Learning Character Recognition with Graph-Based Privileged Information

Westphal

Lavesson

Grahn

2019

2019 International Conference on Document Analysis and Recognition (ICDAR)

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This paper proposes a pre-training method for neural network-based character recognizers to reduce the required amount of training data, and thus the human labeling effort. The proposed method transfers knowledge about the similarities between graph representations of characters to the recognizer by training to predict the graph edit distance. We show that convolutional neural networks trained with this method outperform traditional supervised learning if only ten or less labeled images per class are available. Furthermore, we show that our approach performs up to 33% better than a graph edit distance based recognition approach, even if only one labeled image per class is available.

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“…Then, they used the SVM classification by using the selected features. Maergner et al (10,11) described the graph-based signature verification system. This system was a combined model consisting of key points graphs with approximate graph edit distance and inkball models.…”

Section: Introductionmentioning

confidence: 99%

Identification of Proper and Improper Signatures Using Graph Theory Techniques

Raj¹,

Venugopal²,

Padmapriya³

2022

IJST

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Objectives:To propose an automatic signature identification for off-line signature utilising graph theory approaches. Methods: Scanned signatures (Kaggle, https://www.kaggle.com/divyanshrai/handwritten-signatures/data) are collected for off-line signature data. The method follows pre-processing, vertex point extraction by midpoint traverse method, features extraction using edge, average edge and average edge D-distance and Support Vector Machine (SVM) to classify and predict the true label for the genuine and forged signatures. False Acceptance Ratio (FAR) and False Rejection Ratio (FRR) give the accuracy of the proposed methods. This off-line signature verification method is compared with the deep learning techniques existing in the literature. Findings: Support Vector Machine (SVM) used for classification and results on standard signature datasets like ICDAR (International Conference on Document Analysis and Recognition). The results demonstrate how the proposed strategy outperforms the state-of-the-art already available. Novelty: The proposed approach use the edge distance, average edge distance, and average edge D-distance inbuilt graph structures to extract the feature points. Keywords: Signature images; grid approach; bipartite graph; complete bipartite graph; mid point traverse method Methodology Proposed methodThis section proposes an off-line signature automatic identification method that can be used to identify the signature's authenticity https://www.indjst.org/

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Offline Signature Verification Via Structural Methods: Graph Edit Distance and Inkball Models

Cited by 17 publications

References 26 publications

An integrated approach on verification of signatures using multiple classifiers (SVM and Decision Tree): A multi-classification approach

An integrated approach on verification of signatures using multiple classifiers (SVM and Decision Tree): A multi-classification approach

Learning Character Recognition with Graph-Based Privileged Information

Identification of Proper and Improper Signatures Using Graph Theory Techniques

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