Handwriting-based gender and handedness classification using convolutional neural networks

Demographical handwritings classification has many applications in various disciplines such as biometrics forensics, psychology, archeology, etc. Finding the best features for differentiating subclasses (e.g. men and women) is one of the major problems in handwriting based demographical classificati...

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Vydané v:	Multimedia tools and applications Ročník 80; číslo 28-29; s. 35341 - 35364
Hlavní autori:	Rahmanian, Mina, Shayegan, Mohammad Amin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.11.2021 Springer Nature B.V
Predmet:	1166: Advances of machine learning in data analytics and visual information Processing Archaeology Artificial neural networks Biometrics Classification Computer Communication Networks Computer Science Data Structures and Information Theory Feature extraction Gender Handedness Handwriting Men Multimedia Information Systems Neural networks Psychology Special Purpose and Application-Based Systems Texts Deep learning IAM database Data augmentation Handedness classification Demographic handwriting Gender classification KHATT database CNNs
ISSN:	1380-7501, 1573-7721
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