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AuthorAl-Maadeed, Somaya
AuthorHassaine A.
Available date2022-05-19T10:23:14Z
Publication Date2014
Publication NameEurasip Journal on Image and Video Processing
ResourceScopus
Identifierhttp://dx.doi.org/10.1186/1687-5281-2014-10
URIhttp://hdl.handle.net/10576/31153
AbstractThe classification of handwriting into different categories, such as age, gender, and nationality, has several applications. In forensics, handwriting classification helps investigators focus on a certain category of writers. However, only a few studies have been carried out in this field. Classification of handwriting into a demographic category is generally performed in two steps: feature extraction and classification. The performance of a system depends mainly on the feature extraction step because characterizing features makes it possible to distinguish between writers. In this study, we propose several geometric features to characterize handwritings and use these features to perform the classification of handwritings with regards to age, gender, and nationality. Features are combined using random forests and kernel discriminant analysis. Classification rates are reported on the QUWI dataset, reaching 74.05% for gender prediction, 55.76% for age range prediction, and 53.66% for nationality prediction when all writers produce the same handwritten text and 73.59% for gender prediction, 60.62% for age range prediction, and 47.98% for nationality prediction when each writer produces different handwritten text.
SponsorThis work is supported by the Qatar National Research Fund through National Priority Research Program (NPRP) No. 09 -864 - 1 - 128. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the Qatar National Research Fund or Qatar University.
Languageen
PublisherHindawi Publishing Corporation
SubjectCharacter recognition
Decision trees
Feature extraction
Forecasting
Population statistics
Social sciences
Category Classification
Chain codes
Directional feature
Handwriting analysis
Writer identification
Classification (of information)
TitleAutomatic prediction of age, gender, and nationality in offline handwriting
TypeArticle
Volume Number2014
dc.accessType Abstract Only


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