TY - GEN
T1 - Automated Dating of Medieval Manuscripts with a New Dataset
AU - Madi, Boraq
AU - Atamni, Nour
AU - Tsitrinovich, Vasily
AU - Vasyutinsky-Shapira, Daria
AU - El-Sana, Jihad
AU - Rabaev, Irina
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - Automated manuscript dating is a long-awaited valuable tool for scholars in their research of historical documents.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’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.
AB - Automated manuscript dating is a long-awaited valuable tool for scholars in their research of historical documents.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’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.
KW - Automated dating
KW - Classification
KW - Historical dataset
KW - Historical document images
KW - Regression
UR - http://www.scopus.com/inward/record.url?scp=85204910037&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-70642-4_8
DO - 10.1007/978-3-031-70642-4_8
M3 - Conference contribution
AN - SCOPUS:85204910037
SN - 9783031706417
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 119
EP - 139
BT - Document Analysis and Recognition – ICDAR 2024 Workshops, Proceedings
A2 - Mouchère, Harold
A2 - Zhu, Anna
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Workshops co-located with the 18th International Conference on Document Analysis and Recognition, ICDAR 2024
Y2 - 30 August 2024 through 31 August 2024
ER -