Introduction
Ancient history relies on disciplines such as epigraphy—the study of inscribed texts known as inscriptions—for evidence of the thought, language, society and history of past civilizations1. However, over the centuries, many inscriptions have been damaged to the point of illegibility, transported far from their original location and their date of writing is steeped in uncertainty. Specialist epigraphers must reconstruct the missing text, a process known as text restoration (Fig. 1), and establish the original place and date of writing, tasks known as geographical attribution and chronological attribution, respectively.
Fig. 1: Restoration of a damaged inscription.
Deep learning for epigraphy
Ithaca is a deep neural network architecture trained to simultaneously perform the tasks of textual restoration, geographical attribution and chronological attribution.
The architecture of Ithaca was carefully tailored to each of the three epigraphic tasks, meaningfully handling long-term context information and producing interpretable outputs to enhance the potential for human–machine cooperation.
Fig. 2: Ithaca’s architecture processing the phrase ‘δήμο το αθηναίων’ (‘the people of Athens’).
How does it work?
For the task of restoration, instead of providing historians with a single restoration hypothesis, Ithaca offers a set of the top 20 decoded predictions ranked by probability (Fig. 3a).
For the geographical attribution task, Ithaca classifies the input text among 84 regions, and the ranked list of possible region predictions is visually implemented with both a map and a bar chart (Fig. 3b). Finally, to expand interpretability for the chronological attribution task, instead of outputting a single date value, we predict a categorical distribution over dates (Fig. 3c).
Fig. 3: Ithaca’s outputs.
Conclusions
Ithaca is to our knowledge the first epigraphic restoration and attribution model of its kind. By substantially improving the accuracy and speed of the epigrapher’s pipeline, it may assist the restoration and attribution of newly discovered or uncertain inscriptions, transforming their value as historical sources and helping historians to achieve a more holistic understanding of the distribution and nature of epigraphic habits across the ancient world.
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