@inproceedings{d939c6e38aec451e9c0288408e42e8d1,
title = "Automated Dating of Medieval Manuscripts with a New Dataset",
abstract = "Automated manuscript dating is a long-awaited valuable tool for scholars in their research of historical documents. This study presents a new dataset of medieval Hebrew manuscripts annotated with dates. Our initial experiments focus on documents written in the Ashkenazi square script, allowing us to refine our methodologies in a manageable setting before addressing more complex script types. Also, to accurately reflect the script{\textquoteright}s historical evolution, we adopt a novel classification approach for time periods of varying lengths, which acknowledges the uneven development of the script over time. We perform extensive experimentation with a variety of deep-learning models and show that the regression approach is more appropriate for estimating the date of the manuscript compared to categorical classification.",
keywords = "Automated dating, Classification, Historical dataset, Historical document images, Regression",
author = "Boraq Madi and Nour Atamni and Vasily Tsitrinovich and Daria Vasyutinsky-Shapira and Jihad El-Sana and Irina Rabaev",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; International Workshops co-located with the 18th International Conference on Document Analysis and Recognition, ICDAR 2024 ; Conference date: 30-08-2024 Through 31-08-2024",
year = "2024",
month = jan,
day = "1",
doi = "https://doi.org/10.1007/978-3-031-70642-4_8",
language = "American English",
isbn = "9783031706417",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "119--139",
editor = "Harold Mouch{\`e}re and Anna Zhu",
booktitle = "Document Analysis and Recognition – ICDAR 2024 Workshops, Proceedings",
address = "Germany",
}