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Ithaka: Restoring and attributing ancient texts using deep neural networks

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Restoring and attributing ancient texts using deep neural networks

Χαῖρε! Welcome to Ithaca's interactive interface. Please follow the instructions below to begin restoring and attributing ancient Greek inscriptions. You will also find more information on the Ithaca project, links to the article and examples of Ithaca in action.

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 civilizations. 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. We present Ithaca, the first Deep Neural Network for the textual restoration, geographical and chronological attribution of ancient Greek inscriptions. Ithaca is designed to assist and expand the historian's workflow: its architecture focuses on collaboration, decision support, and interpretability.

While Ithaca alone achieves 62% accuracy when restoring damaged texts, as soon as historians use Ithaca their performance leaps from 25% to 72%, confirming this synergistic research aid's impact. Ithaca can attribute inscriptions to their original findspot with 71% accuracy and can date them with a distance of less than 30 years from ground-truth ranges, redating key texts of Classical Athens and contributing to topical debates in Ancient History.


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