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Proceedings Paper

Writer identification on historical Glagolitic documents
Author(s): Stefan Fiel; Fabian Hollaus; Melanie Gau; Robert Sablatnig
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Paper Abstract

This work aims at automatically identifying scribes of historical Slavonic manuscripts. The quality of the ancient documents is partially degraded by faded-out ink or varying background. The writer identification method used is based on image features, which are described with Scale Invariant Feature Transform (SIFT) features. A visual vocabulary is used for the description of handwriting characteristics, whereby the features are clustered using a Gaussian Mixture Model and employing the Fisher kernel. The writer identification approach is originally designed for grayscale images of modern handwritings. But contrary to modern documents, the historical manuscripts are partially corrupted by background clutter and water stains. As a result, SIFT features are also found on the background. Since the method shows also good results on binarized images of modern handwritings, the approach was additionally applied on binarized images of the ancient writings. Experiments show that this preprocessing step leads to a significant performance increase: The identification rate on binarized images is 98.9%, compared to an identification rate of 87.6% gained on grayscale images.

Paper Details

Date Published: 24 March 2014
PDF: 10 pages
Proc. SPIE 9021, Document Recognition and Retrieval XXI, 902102 (24 March 2014); doi: 10.1117/12.2042338
Show Author Affiliations
Stefan Fiel, Technische Univ. Wien (Austria)
Fabian Hollaus, Technische Univ. Wien (Austria)
Melanie Gau, Technische Univ. Wien (Austria)
Robert Sablatnig, Technische Univ. Wien (Austria)


Published in SPIE Proceedings Vol. 9021:
Document Recognition and Retrieval XXI
Bertrand Coüasnon; Eric K. Ringger, Editor(s)

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