Classification of Handwritten Text Signatures by Person and Gender: A Comparative Study of Transfer Learning Methods

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dc.authoridAydemir, Emrah/0000-0002-8380-7891
dc.contributor.authorAgduk, Sidar
dc.contributor.authorAydemir, Emrah
dc.date.accessioned2025-03-17T12:25:24Z
dc.date.available2025-03-17T12:25:24Z
dc.date.issued2022
dc.departmentTarsus Üniversitesi
dc.description.abstractThe writing process, in which feelings and thoughts are expressed in writing, differs from person to person. Handwriting samples, which are very easy to obtain, are frequently used to identify individuals because they are biometric data. Today, with human-machine interaction increasing by the day, machine learning algorithms are frequently used in offline handwriting identification. Within the scope of this study, a dataset was created from 3250 handwritten images of 65 people. We tried to classify collected handwriting samples according to person and gender. In the classification made for person and gender recognition, feature extraction was done using 32 different transfer learning algorithms in the Python program. For person and gender estimation, the classification process was carried out using the random forest algorithm. 28 different classification algorithms were used, with DenseNet169 yielding the most successful results, and the data were classified in terms of person and gender. As a result, the highest success rates obtained in person and gender classification were 92.46% and 92.77%, respectively.
dc.identifier.doi10.18267/j.aip.197
dc.identifier.endpage347
dc.identifier.issn1805-4951
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85146512098
dc.identifier.scopusqualityQ2
dc.identifier.startpage324
dc.identifier.urihttps://doi.org/10.18267/j.aip.197
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1667
dc.identifier.volume11
dc.identifier.wosWOS:001105633500006
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherUniv Economics, Prague
dc.relation.ispartofActa Informatica Pragensia
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250316
dc.subjectOffline Handwriting Recognition
dc.subjectDenseNet169
dc.subjectMachine Learning
dc.titleClassification of Handwritten Text Signatures by Person and Gender: A Comparative Study of Transfer Learning Methods
dc.typeArticle

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