Inscriptions are a testimony to the past but their poor condition, caused by the deterioration of the material on which they are engraved upon, often makes them partially or completely illegible. The process of restoring these inscriptions is time-consuming and requires the involvement of an expert epigraphist. It is possible to speed-up this process by adopting a semi-automatic assisting tool based on deep neural networks. This work describes a methodology, from the acquisition of the inscriptions to the description of four possible approaches, to predict the missing text in a Latin inscription, that our research team plans to implement in the near future as part of an interdisciplinary research project.
