Update README.md

README.md

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 OCR-to-gold parallel dataset for OCR post-correcting early modern Dutch, originating from EmDComF.
-
-Please cite the following paper when referring to this work:
-@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://aclanthology.org/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."
-}