Abstract

Inks constitute the main element in Medieval manuscripts and their examination and analysis provides an invaluable source of information on the authenticity of the manuscripts, the number of authors involved and dating of the manuscripts. Most existing methods for the analysis of ink materials are based on destructive testing techniques that require the physicochemical sampling of data. Such methods cannot be widely used because of the historical and cultural value of manuscripts. In this paper we present a novel approach for discriminating and identifying inks based on the correlations of image variations under visible and infrared illumination. Such variations are studied using co-occurrence matrices and detect the behavior of the inks during the scripting process.

One of the tasks facing historians and preservationists is the authentication or dating of medieval manuscripts. To this end it is important to verify whether writings on the same or different manuscripts are concurrent. We propose a novel approach for the automated image-based differentiation of inks used in medieval manuscripts. We consider the problem of capturing images of manuscript pages in near-infrared (NIR) spectrum and compare the ink appearance and textural features of segmented text. We present feature descriptors that capture the variability of the visual properties of the inks in NIR based on intensity distributions of histograms and co-occurrence matrices. Our approach is novel as it is entirely image based and does not include the spectrum analysis of the inks. The method is validated by using model ink images manufactured based on known recipes and ink segmented from medieval manuscripts dated from the 11th to the 16th century. Model inks are classified by using both supervised and unsupervised clustering. Comparison of inks of unknown composition is achieved through unsupervised multi-dimensional clustering of the feature descriptors and similarity measures of derived probability density functions.

Statistical classification methods can be applied to images of historical manuscripts in order to

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