BibTeX

@inProceedings{lofgren-dannells-2024-post-336065,
	title        = {Post-OCR Correction of Digitized Swedish Newspapers with ByT5},
	abstract     = {Many collections of digitized newspapers suffer from poor OCR quality, which impacts readability, information retrieval, and analysis of the material. Errors in OCR output can be reduced by applying machine translation models to translate it into a corrected version. Although transformer models show promising results in post-OCR correction and related tasks in other languages, they have not yet been explored for correcting OCR errors in Swedish texts. This paper presents a post-OCR correction model for Swedish 19th to 21th century newspapers based on the pre-trained transformer model ByT5. Three versions of the model were trained on different mixes of training data. The best model, which achieved a 36\% reduction in CER, is made freely available and will be integrated into the automatic processing pipeline of Språkbanken Text, a Swedish language technology infrastructure containing modern and historical written data.},
	booktitle    = {Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024)},
	author       = {Löfgren , Viktoria  and Dannélls, Dana},
	year         = {2024},
	publisher    = {Association for Computational Linguistics},
	address      = {United States Pennsylvania East Stroudsburg},
}