Unsupervised ink type recognition in ancient manuscripts

One of the tasks facing historians and conservationists 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. This work considers the problem of capturing images of manuscript pages in near-infrared (NIR) spectrum and compare the ink appearance of segmented text and their textural features. We present feature descriptors that capture the variability of the visual properties of the inks in NIR. 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.