Authors:
Thomas Reynolds
1
;
Maruf A. Dhali
2
and
Lambert Schomaker
2
Affiliations:
1
Department of Computer Science, Royal Holloway, University of London, U.K.
;
2
Department of Artificial Intelligence, University of Groningen, The Netherlands
Keyword(s):
Document Analysis, Image-Based Material Analysis, Historical Manuscript, Feature Extraction, Fourier Transform, Classification.
Abstract:
Researchers continually perform corroborative tests to classify ancient historical documents based on the physical materials of their writing surfaces. However, these tests, often performed on-site, requires actual access to the manuscript objects. The procedures involve a considerable amount of time and cost, and can damage the manuscripts. Developing a technique to classify such documents using only digital images can be very useful and efficient. In order to tackle this problem, this study uses images from a famous historical collection, the Dead Sea Scrolls, to propose a novel method to classify the materials of the manuscripts. The proposed classifier uses the two-dimensional Fourier Transform to identify patterns within the manuscript surfaces. Combining a binary classification system employing the transform with a majority voting process is shown to be effective for this classification task. This pilot study shows a successful classification percentage of up to 97% for a confi
ned amount of manuscripts produced from either parchment or papyrus material. Feature vectors based on Fourier-space grid representation outperformed a concentric Fourier-space format.
(More)