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Document classification based on what is there and what should be there

Some of the key questions in paleography are those of classification, namely trying to ascertain when and where a given manuscript was written, and — if possible — by whom. Paleographers bring many skills and tools to bear on these questions in what is often a complicated and laborious task requiring reference to paleographic, linguistic and archaeological data, among others. Because it is difficult to quantify the degree of certainty in the final readings and assessments, or even to articulate the arguments underlying these readings, experts have begun to develop computer-based methods for paleographic research in which the description of the various findings is made explicit (Ciula, 2005; Stokes, 2008; Aussems and Brink, 2009; Hofmeister et al., 2009). Some of these computer-based approaches involve little or no human intervention. However, others require manual selection of regions in the image or manual recording of descriptors, that is, of features in the handwriting which are considered significant (Ciula, 2005; Stokes, 2008). Evaluating the significance of the features can be improved using statistical analysis (Levy et al., 2012). Such manual selection raises a key challenge in any system of descriptors, namely that of attribute repeatability among documents of the same category. Would two different people necessarily record the same descriptors for a given sample of writing? Surely some significant features would then be overlooked? If so then what are the implications, both for the accuracy of the results and for the perceived “objectivity” of the method. A descriptor that is marked as existing in a document is likely to exist; however, a descriptor might be unmarked due to an omission or simply because it is not present in the part of the manuscript that is available for inspection. Moreover, even very discriminative descriptors (those which are very important for distinguishing date, location or scribe) might not be present where expected due to scribal variance within the same location and date. In order to overcome this challenge, we suggest a new statistical tool that allows us to hypothesize which attributes should be turned on — in other words, which attributes are likely to have been omitted due to the limits of selection — and then to perform classification on the augmented data. Our results demonstrate that this tool is effective in computer-based document classification.