Dating papyri accurately is crucial not only to editing their texts but also for our understanding of palaeography and the history of writing, ancient scholarship, material culture, networks in antiquity, etc. Most ancient manuscripts offer little evidence regarding the time of their production, forcing papyrologists to date them on palaeographical grounds, a method often criticized for its subjectivity. In this work, with data obtained from the Collaborative Database of Dateable Greek Bookhands, an online collection of objectively dated Greek papyri, we created a dataset of literary papyri, which can be used for computational papyri dating. We also experimented on this dataset, by fine-tuning four convolutional neural networks pre-trained on generic images.

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