Image to interpretation : towards an intelligent system to aid historians in the reading of the Vindolanda texts
The ink and stylus tablets discovered at the Roman Fort of Vindolanda have provided a unique resource for scholars of ancient history. However, the stylus tablets in particular have proved extremely difficult to read. The aim of this thesis is to explore the extent to which techniques from Artificial Intelligence can be used to develop a system that could aid historians in reading the stylus texts. This system would utilise image processing techniques that have been developed in Engineering Science to analyse the stylus tablets, whilst incorporating knowledge elicited from experts working on the texts, to propagate possible suggestions of the text contained within the tablets. This thesis reports on what appears to be the first system developed to aid experts in the process of reading an ancient document. There has been little previous research carried out to see how papyrologists actually carry out their task. This thesis studies closely how experts working with primary sources, such as the Vindolanda Texts, operate. Using Knowledge Elicitation Techniques, a model is proposed for how they read a text. Information regarding the letter forms and language used at Vindolanda is collated, A corpus of annotated images is built up, to provide a data set regarding the letter forms used in the ink and stylus texts. In order to relate this information to the work done on image processing, a stochastic Minimum Description Length (MDL) architecture is adopted, and adapted, to form the basis of a system that can propagate interpretations of the Vindolanda texts. In doing so a system is constructed that can read in image data and output textual interpretations of the writing that appears on the documents. It is demonstrated that knowledge elicitation techniques can be used to capture and mobilise expert information. The process of reading ancient, and ambiguous texts, is made explicit. It is also shown that MDL can be used as a basis to build large systems that reason about complex information effectively. This research presents the first stages towards developing a cognitive visual system that can propagate realistic interpretations from image data, and so aid the papyrologists in their task.