  Ancient history relies on disciplines such as epigraphy, the study of ancient
inscribed texts, for evidence of the recorded past. However, these texts,
"inscriptions", are often damaged over the centuries, and illegible parts of
the text must be restored by specialists, known as epigraphists. This work
presents Pythia, the first ancient text restoration model that recovers missing
characters from a damaged text input using deep neural networks. Its
architecture is carefully designed to handle long-term context information, and
deal efficiently with missing or corrupted character and word representations.
To train it, we wrote a non-trivial pipeline to convert PHI, the largest
digital corpus of ancient Greek inscriptions, to machine actionable text, which
we call PHI-ML. On PHI-ML, Pythia's predictions achieve a 30.1% character error
rate, compared to the 57.3% of human epigraphists. Moreover, in 73.5% of cases
the ground-truth sequence was among the Top-20 hypotheses of Pythia, which
effectively demonstrates the impact of this assistive method on the field of
digital epigraphy, and sets the state-of-the-art in ancient text restoration.
