@inproceedings{kanerva-etal-2025-ocr,
    title = "{OCR} Error Post-Correction with {LLM}s in Historical Documents: No Free Lunches",
    author = {Kanerva, Jenna  and
      Ledins, Cassandra  and
      K{\"a}pyaho, Siiri  and
      Ginter, Filip},
    editor = "Holdt, {\v{S}}pela Arhar  and
      Ilinykh, Nikolai  and
      Scalvini, Barbara  and
      Bruton, Micaella  and
      Debess, Iben Nyholm  and
      Tudor, Crina Madalina",
    booktitle = "Proceedings of the Third Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2025)",
    month = mar,
    year = "2025",
    address = "Tallinn, Estonia",
    publisher = "University of Tartu Library, Estonia",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.resourceful-1.8/",
    pages = "38--47",
    ISBN = "978-9908-53-121-2",
    abstract = "Optical Character Recognition (OCR) systems often introduce errors when transcribing historical documents, leaving room for post-correction to improve text quality. This study evaluates the use of open-weight LLMs for OCR error correction in historical English and Finnish datasets. We explore various strategies, including parameter optimization, quantization, segment length effects, and text continuation methods. Our results demonstrate that while modern LLMs show promise in reducing character error rates (CER) in English, a practically useful performance for Finnish was not reached. Our findings highlight the potential and limitations of LLMs in scaling OCR post-correction for large historical corpora."
}