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AuthorKhalifa, E.
AuthorAl-Maadeed, S.
AuthorTahir, M.A.
AuthorBouridane, A.
AuthorJamshed, A.
Available date2023-02-23T09:13:04Z
Publication Date2015
Publication NamePattern Recognition Letters
ResourceScopus
URIhttp://dx.doi.org/10.1016/j.patrec.2015.03.004
URIhttp://hdl.handle.net/10576/40335
AbstractOff-line writer identification is the process of matching a handwritten sample with its author. Manual identification is very time-consuming because it requires a meticulous comparison of character shape details. Consequently the automation of writer identification has become an important area of research interest. The codebook (or bag of features) approach is a state-of-the-art computerized technique for writer identification. One way to achieve a high identification rate is to expose the personalized set of character shapes, or allographs, that a writer has adopted over the years. The main problem associated with this approach is the extremely large of number of points of interest that are generated. In this paper we extend the basic model to include an ensemble of codebooks. Additionally, Kernel discriminant analysis using spectral regression (SR-KDA) is used as a dimensionality reduction technique in order to avoid over-fitting. Fusion of multiple codebooks is shown to increase the identification rate by 11% compared with a single codebook approach.
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
PublisherElsevier
SubjectForensic document examination
Grapheme features
Kernel discriminant analysis
Writer identification
TitleOff-line writer identification using an ensemble of grapheme codebook features
TypeArticle
Pagination18-25
Volume Number59
dc.accessType Abstract Only


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