Recognizing Cuneiform Signs Using Graph Based Methods

Abstract

The cuneiform script constitutes one of the earliest systems of writing and is realized by wedge-shaped marks on clay tablets. A tremendous number of cuneiform tablets have already been discovered and are incrementally digitalized and made available to automated processing. As reading cuneiform script is still a manual task, we address the real-world application of recognizing cuneiform signs by two graph based methods with complementary runtime characteristics. We present a graph model for cuneiform signs together with a tailored distance measure based on the concept of the graph edit distance. We propose efficient heuristics for its computation and demonstrate its effectiveness in classification tasks experimentally. To this end, the distance measure is used to implement a nearest neighbor classifier leading to a high computational cost for the prediction phase with increasing training set size. In order to overcome this issue, we propose to use CNNs adapted to graphs as an alternative approach shifting the computational cost to the training phase. We demonstrate the practicability of both approaches in an experimental comparison regarding runtime and prediction accuracy. Although currently available annotated real-world data is still limited, we obtain a high accuracy using CNNs, in particular, when the training set is enriched by augmented examples.

A valuable contribution to automated analysis of palaeolithic artefacts

“…This research serves as a first step towards abstracting human lithic tools in a simplified and computerreadable way. We do consider our graph models of individual tools as intermediate result being a data source for graph-based machine learning, which has shown great potential in recognizing cuneiform signs (Kriege et al, 2018), and ultimately as a stepping stone towards a more detailed understanding of lithic technologies.…”