Automated Dating of Medieval Manuscripts with a New Dataset

Boraq Madi, Nour Atamni, Vasily Tsitrinovich, Daria Vasyutinsky-Shapira, Jihad El-Sana, Irina Rabaev

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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Automated manuscript dating is a long-awaited valuable tool for scholars in their research of historical documents.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’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.

Original languageAmerican English
Title of host publicationDocument Analysis and Recognition – ICDAR 2024 Workshops, Proceedings
EditorsHarold Mouchère, Anna Zhu
PublisherSpringer Science and Business Media Deutschland GmbH
Pages119-139
Number of pages21
ISBN (Print)9783031706417
DOIs
StatePublished - 1 Jan 2024
EventInternational Workshops co-located with the 18th International Conference on Document Analysis and Recognition, ICDAR 2024 - Athens, Greece
Duration: 30 Aug 202431 Aug 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14936 LNCS

Conference

ConferenceInternational Workshops co-located with the 18th International Conference on Document Analysis and Recognition, ICDAR 2024
Country/TerritoryGreece
CityAthens
Period30/08/2431/08/24

Keywords

  • Automated dating
  • Classification
  • Historical dataset
  • Historical document images
  • Regression

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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