A convolution deep architecture for gender classification of urdu handwritten characters.
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| Title: | A convolution deep architecture for gender classification of urdu handwritten characters. |
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| Authors: | Nabi, Syed Tufael1 (AUTHOR), Kumar, Munish2 (AUTHOR) munishcse@gmail.com, Singh, Paramjeet1 (AUTHOR) |
| Source: | Multimedia Tools & Applications. Sep2024, Vol. 83 Issue 29, p72179-72194. 16p. |
| Subjects: | Graphology, Automatic classification, Computer science, Deep learning, Psychological factors |
| Abstract: | Writing is a commonplace activity that individuals partake in regularly. However, the implications behind it are often overlooked. When we write, various psychological factors come into play as the pen creates letters on the paper. Handwriting analysis has long been a subject of study, attracting researchers from diverse disciplines such as graphology, psychology, paleography, neuroscience, criminology, and computer science. Among the promising applications of handwriting analysis is gender classification, where a system can predict the gender of a writer based on their handwriting style. Since each individual's handwriting is unique, and variations exist between the handwriting of different genders, an automatic gender classification system can exploit these differences to make predictions. This paper presents a deep-learning-based gender classification system specifically designed for Urdu handwriting. The proposed approach utilizes a CNN network trained and tested on a self-created dataset contributed by 200 distinct male and 200 female Urdu writers. Through this method, the gender classification system achieved an impressive overall accuracy of 99.63%. The results obtained demonstrate that our technique for Urdu handwriting-based writer identification surpasses existing approaches. In the future, we intend to explore transfer learning techniques to further advance this field. [ABSTRACT FROM AUTHOR] |
| Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 179394067 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A convolution deep architecture for gender classification of urdu handwritten characters. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Nabi%2C+Syed+Tufael%22">Nabi, Syed Tufael</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kumar%2C+Munish%22">Kumar, Munish</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> munishcse@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Singh%2C+Paramjeet%22">Singh, Paramjeet</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. Sep2024, Vol. 83 Issue 29, p72179-72194. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Graphology%22">Graphology</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+classification%22">Automatic classification</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+science%22">Computer science</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Psychological+factors%22">Psychological factors</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Writing is a commonplace activity that individuals partake in regularly. However, the implications behind it are often overlooked. When we write, various psychological factors come into play as the pen creates letters on the paper. Handwriting analysis has long been a subject of study, attracting researchers from diverse disciplines such as graphology, psychology, paleography, neuroscience, criminology, and computer science. Among the promising applications of handwriting analysis is gender classification, where a system can predict the gender of a writer based on their handwriting style. Since each individual's handwriting is unique, and variations exist between the handwriting of different genders, an automatic gender classification system can exploit these differences to make predictions. This paper presents a deep-learning-based gender classification system specifically designed for Urdu handwriting. The proposed approach utilizes a CNN network trained and tested on a self-created dataset contributed by 200 distinct male and 200 female Urdu writers. Through this method, the gender classification system achieved an impressive overall accuracy of 99.63%. The results obtained demonstrate that our technique for Urdu handwriting-based writer identification surpasses existing approaches. In the future, we intend to explore transfer learning techniques to further advance this field. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11042-024-18415-5 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 72179 Subjects: – SubjectFull: Graphology Type: general – SubjectFull: Automatic classification Type: general – SubjectFull: Computer science Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Psychological factors Type: general Titles: – TitleFull: A convolution deep architecture for gender classification of urdu handwritten characters. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nabi, Syed Tufael – PersonEntity: Name: NameFull: Kumar, Munish – PersonEntity: Name: NameFull: Singh, Paramjeet IsPartOfRelationships: – BibEntity: Dates: – D: 11 M: 09 Text: Sep2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 13807501 Numbering: – Type: volume Value: 83 – Type: issue Value: 29 Titles: – TitleFull: Multimedia Tools & Applications Type: main |
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