These tools will make it possible to examine parchments and coins down to the finest detail, at the pixel level. These in-depth analyses therefore generate a colossal volume of raw data to process. This is where artificial intelligence comes into play to speed up the processing and reveal the information “hidden” in the data, identifying major trends invisible to the naked eye.
Above all, it will provide a boost in meeting the challenge of dating the objects under study. Dated documents, such as charters, will thus be used as references to test the model’s robustness by comparing the results obtained with already known dates. “If the results are convincing, the technique could be applied to undated documents,” says Nicolas Ruffini-Ronzani. This would represent a significant breakthrough in historical research.
“The use of machine learning methods is not a panacea,” Olivier Deparis qualifies, however. “We wanted to explore it as an open-ended question to assess its benefits.”
PHOENIX could thus herald a new era for heritage sciences, where artificial intelligence—much like the phoenix after which the project is named—opens up new ways to analyze and understand materials from the past.