Evaluation of Classification Performance of New Layered Convolutional Neural Network Architecture on Offline Handwritten Signature Images

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dc.authoriderdogmus, pakize/0000-0003-2172-5767en_US
dc.authoridOZKAN, Yasin/0000-0002-2029-0856en_US
dc.authorscopusid58313776800en_US
dc.authorscopusid35789879200en_US
dc.contributor.authorOzkan, Yasin
dc.contributor.authorErdogmus, Pakize
dc.date.accessioned2024-08-23T16:03:35Z
dc.date.available2024-08-23T16:03:35Z
dc.date.issued2024en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractWhile there are many verification studies on signature images using deep learning algorithms in the literature, there is a lack of studies on the classification of signature images. Signatures are used as a means of identification for banking, security controls, symmetry, certificates, and contracts. In this study, the aim was to design network architectures that work very fast in areas that require only signature images. For this purpose, a new Si-CNN network architecture with existing layers was designed. Afterwards, a new loss function and layer (Si-CL), a novel architecture using Si-CL as classification layer in Si-CNN to increase the performance of this architecture, was designed. This architecture was called Si-CNN+NC (New Classification). Si-CNN and Si-CNN+NC were trained with two datasets. The first dataset which was used for training is the C-Signatures (Classification Signatures) dataset, which was created to test these networks. The second dataset is the Cedar dataset, which is a benchmark dataset. The number of classes and sample numbers in the two datasets are symmetrical with each other. To compare the performance of the trained networks, four of the most well-known pre-trained networks, GoogleNet, DenseNet201, Inceptionv3, and ResNet50, were also trained with the two datasets with transfer learning. The findings of the study showed that the proposed network models can learn features from two different handwritten signature images and achieve higher accuracy than other benchmark models. The test success of the trained networks showed that the Si-CNN+NC network outperforms the others, in terms of both accuracy and speed. Finally, Si-CNN and Si-CNN+NC networks were trained with the gold standard dataset MNIST and showed superior performance. Due to its superior performance, Si-CNN and Si-CNN+NC can be used by signature experts as an aid in a variety of applications, including criminal detection and forgery.en_US
dc.identifier.doi10.3390/sym16060649
dc.identifier.issn2073-8994
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85197923027en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/sym16060649
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13797
dc.identifier.volume16en_US
dc.identifier.wosWOS:001256764800001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofSymmetry-Baselen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectconvolutional neural networksen_US
dc.subjectclassificationen_US
dc.subjectsignatureen_US
dc.subjectcedar signatureen_US
dc.subjectVerificationen_US
dc.subjectFeaturesen_US
dc.titleEvaluation of Classification Performance of New Layered Convolutional Neural Network Architecture on Offline Handwritten Signature Imagesen_US
dc.typeArticleen_US

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