The EU funded EAGLE Project (www.eagle-network.eu) - whose data aggregation and image processing infrastructure was implemented by the NeMIS Lab of ISTI-CNR - won the Digital Humanities Awards 2016 (dhawards.org/dhawards2016/results/) for the "Best DH Tool or Suite of Tools" category.

Digital Humanities Awards are a set of annual awards where the public is able to nominate resources for the recognition of talent and expertise in the digital humanities community. The resources are nominated and voted for entirely by the public. These awards are intended as an awareness raising activity, to help put interesting DH resources in the spotlight and engage DH users (and general public) in the work of the community. Awards are not specific to geography, language, conference, organization or field of humanities that they benefit. Any suitable resource in any language or writing system may be nominated in any category.

EAGLE (Europeana network of Ancient Greek and Latin Epigraphy, a Best Practice Network partially funded by the European Commission) aggregates epigraphic material provided by some 15 different epigraphic archives (about 80% of the classified epigraphic material from the European and Mediterranean area) for ingestion to Europeana (www.europeana.eu/). The aggregated material is made available to the scholarly community and the general public for research and cultural dissemination. EAGLE has defined a common data model for epigraphic information, into which data models from different archives can be optimally mapped. The data infrastructure is based on the D-NET software toolkit (www.d-net.research-infrastructures.eu) developed by the InfraScience Research Group of the NeMIS Lab, which handles data collection, mapping, cleaning, indexing, and access provisioning through web portals or standard access protocols. A novel search feature offered by EAGLE and developed by the Multimedia Information Retrieval Research Group of the NeMIS Lab is the possibility of visually searching for epigraphic information. A picture of an inscription can be used as a query to search for similar inscriptions or to obtain information on it. Visual queries can also be issued from a smartphone, by pointing its embedded camera toward the inscription of interest. Visual inscription search leverages jointly on deep learning techniques and index structures for large scale similarity searching. Currently, more than one million epigraphs are visually searchable with EAGLE.