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Handwriting Based Gender Classification Using COLD and Hinge Features

Gender Classification from handwriting is still considered to be challenging due to homogeneous vision comparing male and female handwritten documents. This paper presents a new method based on Cloud of Line Distribution (COLD) and Hinge feature for distinguishing the gender from handwriting. The SV...

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Detalles Bibliográficos
Autores principales: Gattal, Abdeljalil, Djeddi, Chawki, Bensefia, Ameur, Ennaji, Abdellatif
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7340951/
http://dx.doi.org/10.1007/978-3-030-51935-3_25
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author Gattal, Abdeljalil
Djeddi, Chawki
Bensefia, Ameur
Ennaji, Abdellatif
author_facet Gattal, Abdeljalil
Djeddi, Chawki
Bensefia, Ameur
Ennaji, Abdellatif
author_sort Gattal, Abdeljalil
collection PubMed
description Gender Classification from handwriting is still considered to be challenging due to homogeneous vision comparing male and female handwritten documents. This paper presents a new method based on Cloud of Line Distribution (COLD) and Hinge feature for distinguishing the gender from handwriting. The SVM classifier combination decides the assigned class based on the maximum of the two decisions values resulting from COLD and Hinge feature. The proposed approach is evaluated on the standard QUWI dataset and following the framework protocol described in the ICFHR 2016 competition. Obtained results are promising regarding the classification rates announced in the literature.
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spelling pubmed-73409512020-07-08 Handwriting Based Gender Classification Using COLD and Hinge Features Gattal, Abdeljalil Djeddi, Chawki Bensefia, Ameur Ennaji, Abdellatif Image and Signal Processing Article Gender Classification from handwriting is still considered to be challenging due to homogeneous vision comparing male and female handwritten documents. This paper presents a new method based on Cloud of Line Distribution (COLD) and Hinge feature for distinguishing the gender from handwriting. The SVM classifier combination decides the assigned class based on the maximum of the two decisions values resulting from COLD and Hinge feature. The proposed approach is evaluated on the standard QUWI dataset and following the framework protocol described in the ICFHR 2016 competition. Obtained results are promising regarding the classification rates announced in the literature. 2020-06-05 /pmc/articles/PMC7340951/ http://dx.doi.org/10.1007/978-3-030-51935-3_25 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Gattal, Abdeljalil
Djeddi, Chawki
Bensefia, Ameur
Ennaji, Abdellatif
Handwriting Based Gender Classification Using COLD and Hinge Features
title Handwriting Based Gender Classification Using COLD and Hinge Features
title_full Handwriting Based Gender Classification Using COLD and Hinge Features
title_fullStr Handwriting Based Gender Classification Using COLD and Hinge Features
title_full_unstemmed Handwriting Based Gender Classification Using COLD and Hinge Features
title_short Handwriting Based Gender Classification Using COLD and Hinge Features
title_sort handwriting based gender classification using cold and hinge features
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7340951/
http://dx.doi.org/10.1007/978-3-030-51935-3_25
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