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24 March 2014Writer identification on historical Glagolitic documents
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.
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Stefan Fiel, Fabian Hollaus, Melanie Gau, Robert Sablatnig, "Writer identification on historical Glagolitic documents," Proc. SPIE 9021, Document Recognition and Retrieval XXI, 902102 (24 March 2014); https://doi.org/10.1117/12.2042338