Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/1753
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dc.contributor.authorDagher, Issamen_US
dc.contributor.authorAbu Jamra, Samiren_US
dc.date.accessioned2020-12-23T08:59:04Z-
dc.date.available2020-12-23T08:59:04Z-
dc.date.issued2019-
dc.identifier.urihttps://scholarhub.balamand.edu.lb/handle/uob/1753-
dc.description.abstractHandwriting recognition is a very active research in the machine learning community. In this paper, we tackled two important applications: handwritten digit recognition and Signature verification using convolution neural network (CNN). Signature is one of the most popular personal attributes for authentication. It is basic, shabby and adequate to individuals, official associations and courts. This paper focuses on offline signature verification (SV). It is a kind of a classification problem, which classifies the signature as genuine, or forgery. We use CNN in two types of datasets: the MNIST database, and UTSIG database. In order to obtain better accuracy, we propose to preprocess the data in the wavelet domain and in the Gabor filter combining the outputs of both CNN. This combination resulted in higher recognition accuracy compared to other techniques.en_US
dc.language.isoengen_US
dc.subjectCNNen_US
dc.subjectHandwritten recognitionen_US
dc.subjectSignature verificationen_US
dc.subjectWaveletsen_US
dc.subjectGaboren_US
dc.titleCombined wavelet and gabor convolution neural networksen_US
dc.typeJournal Articleen_US
dc.contributor.affiliationDepartment of Computer Engineeringen_US
dc.description.volume17en_US
dc.description.issue6en_US
dc.date.catalogued2020-02-17-
dc.description.statusPublisheden_US
dc.identifier.OlibID252401-
dc.relation.ispartoftextInternational journal of wavelets multiresolution and information processingen_US
dc.provenance.recordsourceOliben_US
crisitem.author.parentorgFaculty of Engineering-
Appears in Collections:Department of Computer Engineering
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