Writer age estimation through handwriting

Huang, Z; Shivakumara, P; Kaljahi, MA; Kumar, A; Pal, U; Lu, T; Blumenstein, M

Writer age estimation through handwriting

Huang, Z Shivakumara, P Kaljahi, MA Kumar, A Pal, U Lu, T Blumenstein, M

Permalink

Publisher:: Springer Nature
Publication Type:: Journal Article
Citation:: Multimedia Tools and Applications, 2022, pp. 1-23
Issue Date:: 2022-01-01

Closed Access

	Filename	Description	Size
	s11042-022-13840-w.pdf	Published version	2.26 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Huang, Z
dc.contributor.author	Shivakumara, P
dc.contributor.author	Kaljahi, MA
dc.contributor.author	Kumar, A
dc.contributor.author	Pal, U
dc.contributor.author	Lu, T
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744
dc.date.accessioned	2023-03-27T01:50:06Z
dc.date.available	2023-03-27T01:50:06Z
dc.date.issued	2022-01-01
dc.identifier.citation	Multimedia Tools and Applications, 2022, pp. 1-23
dc.identifier.issn	1380-7501
dc.identifier.issn	1573-7721
dc.identifier.uri	http://hdl.handle.net/10453/168516
dc.description.abstract	Handwritten image-based writer age estimation is a challenging task due to the various writing styles of different individuals, use of different scripts, varying alignment, etc. Unlike age estimation using face recognition in biometrics, handwriting-based age classification is reliable and inexpensive because of the plain backgrounds of documents. This paper presents a novel model for deriving the phase spectrum based on the Harmonic Wavelet Transform (HWT) for age classification on handwritten images from 11 to 65 years. This includes 11 classes with an interval of 5 years. In contrast to the Fourier transform, which provides a noisy phase spectrum due to loss of time variations, the proposed HWT-based phase spectrum retains time variations of phase and magnitude. As a result, the proposed HWT-based phase spectrum preserves vital information of changes in handwritten images. In order to extract such information, we propose new phase statistics-based features for age classification based on the understanding that as age changes, writing style also changes. The features and the input images are fed to a VGG-16 model for age classification. The proposed method is tested on our own dataset and three standard datasets, namely, IAM-2, KHATT and that of Basavaraja et al. to demonstrate the effectiveness of the proposed model compared to the existing methods in terms of classification rate. The results of the proposed and existing methods on different datasets show that the proposed method outperforms the existing methods in terms of classification rate.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Multimedia Tools and Applications
dc.relation.isbasedon	10.1007/s11042-022-13840-w
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0803 Computer Software, 0805 Distributed Computing, 0806 Information Systems
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	Software Engineering
dc.title	Writer age estimation through handwriting
dc.type	Journal Article
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-27T01:50:05Z
pubs.publication-status	Published

Abstract:

Handwritten image-based writer age estimation is a challenging task due to the various writing styles of different individuals, use of different scripts, varying alignment, etc. Unlike age estimation using face recognition in biometrics, handwriting-based age classification is reliable and inexpensive because of the plain backgrounds of documents. This paper presents a novel model for deriving the phase spectrum based on the Harmonic Wavelet Transform (HWT) for age classification on handwritten images from 11 to 65 years. This includes 11 classes with an interval of 5 years. In contrast to the Fourier transform, which provides a noisy phase spectrum due to loss of time variations, the proposed HWT-based phase spectrum retains time variations of phase and magnitude. As a result, the proposed HWT-based phase spectrum preserves vital information of changes in handwritten images. In order to extract such information, we propose new phase statistics-based features for age classification based on the understanding that as age changes, writing style also changes. The features and the input images are fed to a VGG-16 model for age classification. The proposed method is tested on our own dataset and three standard datasets, namely, IAM-2, KHATT and that of Basavaraja et al. to demonstrate the effectiveness of the proposed model compared to the existing methods in terms of classification rate. The results of the proposed and existing methods on different datasets show that the proposed method outperforms the existing methods in terms of classification rate.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/168516