@inproceedings{debaene-etal-2025-evaluating,
    title = "Evaluating Transformers for {OCR} Post-Correction in Early {M}odern {D}utch Theatre",
    author = "Debaene, Florian  and
      Maladry, Aaron  and
      Lefever, Els  and
      Hoste, Veronique",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.coling-main.690/",
    pages = "10367--10374",
    abstract = "This paper explores the effectiveness of two types of transformer models {---} large generative models and sequence-to-sequence models {---} for automatically post-correcting Optical Character Recognition (OCR) output in early modern Dutch plays. To address the need for optimally aligned data, we create a parallel dataset based on the OCRed and ground truth versions from the EmDComF corpus using state-of-the-art alignment techniques. By combining character-based and semantic methods, we design and release a qualitative OCR-to-gold parallel dataset, selecting the alignment with the lowest Character Error Rate (CER) for all alignment pairs. We then fine-tune and evaluate five generative models and four sequence-to-sequence models on the OCR post-correction dataset. Results show that sequence-to-sequence models generally outperform generative models in this task, correcting more OCR errors and overgenerating and undergenerating less, with mBART as the best performing system."
}