Probabilistic image-based characterization of manuscript inks

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.

An ink texture descriptor for NIR-imaged medieval documents

“…Hereafter we refer to these as the model images. Each model image was generated with different ink types [1] and under various writing conditions(see figure 4b). From figure 4a the reader can observe how each of the ten layers increases in ink density from top to bottom.…”

“…Computer vision techniques can be used as alternative diagnostic methods by computing models and interpreting the visual properties of the material used such as inks. In an early approach Kokla et al studied techniques for image-based ink classification of historical documents using statistical modelling of ink intensity using Gaussian mixtures [1]. In a later work, the same authors consider co-occurrence matrices of ink intensities as models of the joint probability of adjacent ink pixels in order to represent the spreading behaviour of writing inks and classify eight specific ink compositions [2].…”