@inproceedings{miller-etal-2025-systematic,
    title = "Systematic Textual Availability of Manuscripts",
    author = "Miller, Hadar  and
      Londner, Samuel  and
      Kuflik, Tsvi  and
      Shapira, Daria Vasyutinsky  and
      Dershowitz, Nachum  and
      Lavee, Moshe",
    editor = "Alam, Mehwish  and
      Tchechmedjiev, Andon  and
      Gracia, Jorge  and
      Gromann, Dagmar  and
      di Buono, Maria Pia  and
      Monti, Johanna  and
      Ionov, Maxim",
    booktitle = "Proceedings of the 5th Conference on Language, Data and Knowledge",
    month = sep,
    year = "2025",
    address = "Naples, Italy",
    publisher = "Unior Press",
    url = "https://preview.aclanthology.org/ldl-25-ingestion/2025.ldk-1.18/",
    pages = "162--173",
    ISBN = "978-88-6719-333-2",
    abstract = "33 The digital era has made millions of manuscript images in Hebrew available to all. However, despite major advancements in handwritten text recognition over the past decade, an efficient pipeline for large scale and accurate conversion of these manuscripts into useful machine-readable form is still sorely lacking.We propose a pipeline that significantly improves recognition models for automatic transcription of Hebrew manuscripts. Transfer learning is used to fine-tune pretrained models. For post-recognition correction, it leverages text reuse, a common phenomenon in medieval manuscripts, and state-of-the-art large language models for medieval Hebrew.The framework successfully handles noisy transcriptions and consistently suggests alternate, better readings. Initial results show that word level accuracy increased by 10{\%} for new readings proposed by text-reuse detection. Moreover, the character level accuracy improved by 18{\%} by fine-tuning models on the first few pages of each manuscript."
}