TY - GEN
T1 - Greek Literary Papyri Dating Benchmark
AU - Paparrigopoulou, Asimina
AU - Kougia, Vasiliki
AU - Konstantinidou, Maria
AU - Pavlopoulos, John
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - computational dating
KW - image classification
KW - papyri images
UR - http://www.scopus.com/inward/record.url?scp=85173054225&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-031-41498-5_21
DO - https://doi.org/10.1007/978-3-031-41498-5_21
M3 - Contribution to proceedings
SN - 9783031414978
T3 - Lecture Notes in Computer Science
SP - 296
EP - 306
BT - Document Analysis and Recognition – ICDAR 2023 Workshops, Proceedings
A2 - Coustaty, Mickael
A2 - Fornés, Alicia
PB - Springer Nature Switzerland AG
CY - Cham
ER -