By bringing together the most prominent European institutions and archives in the eld of Classical Latin and Greek epigraphy, the EAGLE project has collected the vast majority of the surviving Greco-Latin inscriptions into a single readily-searchable database. Text-based search engines are typically used to retrieve information about ancient inscriptions (or about other artifacts). These systems require that the users formulate a text query that contains information such as the place where the object was found or where it is currently located. Conversely, visual search systems can be used to provide information to users (like tourists and scholars) in a most intuitive and immediate way, just using an image as query. In this paper, we provide a comparison of several approaches for visual recognizing ancient inscriptions. Our experiments, conducted on 17,155 photos related to 14,560 inscriptions, show that BoW and VLAD are outperformed by both Fisher Vector (FV) and Convolutional Neural Network (CNN) features. More interestingly, combining FV and CNN features into a single image representation allows achieving very high eff ectiveness by correctly recognizing the query inscription in more than 90% of the cases. Our results suggest that combinations of FV and CNN can be also exploited to e ffectively perform visual retrieval of other types of objects
related to cultural heritage such as landmarks and monuments.
Keywords:{ Retrieval on mobile devices; Image search;Visual content-based indexing and retrieval; Fisher Vector; Convolutional Neural Network; Epigraphy; Latin and Greek inscriptions}
