Ink recognition based on statistical classification methods

Statistical classification methods can be applied to images of historical manuscripts in order to characterize the various kinds of inks used. As these methods do not require destructive sampling they can be applied to the study of old and fragile manuscripts. Analysis of manuscript inks based on statistical analysis can be applied in situ, to provide important information for the authenticity, dating and origin of manuscripts. This paper describes a methodology and related algorithms used to interpret the photometric properties of inks and produce computational models which classify diverse types of inks found in Byzantine-era manuscripts. Various optical properties of these inks are extracted by the analysis of digital images taken in the visible and infrared regions of the electromagnetic spectrum. The inks are modelled based on their grey-level and colour information using a mixture of Gaussian functions and classified using Bayes' decision rule

Ink discrimination based on co-occurrence analysis of visible and infrared images

Inks found in Byzantine manuscripts are semitransparent pigments and their examination and analysis provide an invaluable source of information on the authenticity and dating of manuscripts and the number of authors involved. However, inks are difficult to characterize because their intensity depends on the amount of liquid spread during scripting and the reflective properties of the support. 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 the manuscripts. In this work we show that manuscript inks can be represented through a mixture of Gaussian functions and can be characterised using co-occurrence matrices

Revealing the visually unknown in ancient manuscripts with a similarity measure for IR-imaged inks

One of the tasks facing historians and conservationists is the authentication or dating of medieval manuscripts. To this end it is important to them to verify whether writings on the same or different manuscripts are concurrent. In this work we explore this task by capturing images of manuscript pages in infrared (IR) and modelling and then comparing the ink appearance of segmented text. The modelling of the text appearance relies on the unsupervised multimodal clustering of ink descriptors and the derived probability density functions. The similarity measure is built around the distribution of cluster labels and their proportions. We demonstrate our method by using both model inks of known composition and authentic Byzantine manuscripts

An ink texture descriptor for nir-imaged medieval documents

In this work we explore the task of authenticating and dating ancient manuscripts by capturing images of pages in nearinfrared (NIR) and modelling and then comparing the ink appearance of segmented text. We present a texture feature descriptor to characterize and recognize semi-transparent materials such as the inks found in manuscripts. These textural patterns are different in nature from perceptual entities such as textons, tokens, frequency or repeatability of textural elements. Our ink texture descriptor relates a set of ink features from various first and second-order statistics to semi-liquid and viscous image-based properties of inks. In particular, we propose eigen features from the joint gray-level probabilities and off-diagonal sums of co-occurrence matrices. We test the qualities of the features with a classifier trained with the ink descriptor to show how well it recognizes eight different inks of known composition. Presented with the very same task the human visual system would fail to spot the ink composition difference given the inks inter-class and intra-class distances are extremely short

ENRICHMENT AND POPULATION OF A GEOSPATIAL ONTOLOGY FOR SEMANTIC INFORMATION EXTRACTION

The massive amount of user-generated content available today presents a new challenge for the geospatial domain and a great opportunity to delve into linguistic, semantic, and cognitive aspects of geographic information. Ontology-based information extraction is a new, prominent field in which a domain ontology guides the extraction process and the identification of pre-defined concepts, properties, and instances from natural language texts. The paper describes an approach for enriching and populating a geospatial ontology using both a top-down and a bottom-up approach in order to enable semantic information extraction. The top-down approach is applied in order to incorporate knowledge from existing ontologies. The bottom-up approach is applied in order to enrich and populate the geospatial ontology with semantic information (concepts, relations, and instances) extracted from domain-specific web content