Advances in Computational Cuneiform Analysis
Channel: Recording Archaeology
Date: 2019-11-05

Motivated by the demand in the Digital Humanities for quantifiable and computational analyses, we present a work-flow and principal tools for digital large-scale processing of cuneiform. Cuneiform tablets appertain to the oldest textual artifacts used for more than three millennia and are comparable in amount and relevance to texts written in Latin or ancient Greek. We present a complete digital analysis work-flow enabling modern text processing on the complex two-dimensional script. Our tools encompass the whole digital pipeline, from digitalization and wedge extraction to word-spotting and frequent pattern mining facilities. Tablets are being acquired from different sources requiring different methods for digitalization. Each representation is typically processed with its own tool-set. To homogenize these data sources, we introduce an unifying minimal wedge feature description. For this representation, we develop similarity metrics based on the optimal assignment of wedge configurations. Since this metric is also a dot-product in high-dimensional space, it enables the use of common kernel-based machine learning algorithms. We combine our feature vectors with work on segmentation-free word spotting using part-structured models. A tree representation of features is used to locate symbols on a dataset of tablets. Such a feature representation is applicable to any geometrical shape enabling the search of arbitrary geometrical objects. Common analytical tasks such as search, counting, and mining co-occurrences, not only of complex wedge constellations, but also of repeating geometric shapes are automated, and can therefore be feasibly performed on large datasets.

(Bartosz Bogacz, Hubert Mara)