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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Veröffentlicht in:Multimedia tools and applications Jg. 80; H. 28-29; S. 35341 - 35364
Hauptverfasser: Rahmanian, Mina, Shayegan, Mohammad Amin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.11.2021
Springer Nature B.V
Schlagworte:
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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Zusammenfassung: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 classification. Convolutional Neural Networks (CNNs) advanced models have a higher capacity in extracting appropriate features compared to traditional models. In this paper, the ability and capacity of deep CNNs in automatic classification of two handwriting based demographical problems, i.e. gender and handedness classification, have been examined by using advanced CNNs; DenseNet201, InceptionV3, and Xception. Two databases, IAM (English texts) and KHATT (Arabic texts) have been employed in this study. The achieved results showed that the proposed CNNs architectures performed well in improving classification results, with 84% accuracy (1.27% improvement) for gender classification using the IAM database, and 99.14% accuracy (28.23% improvement) for handedness classification using the KHATT database.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-020-10170-7