@inproceedings{thomas-etal-2024-leveraging,
    title = "Leveraging {LLM}s for Post-{OCR} Correction of Historical Newspapers",
    author = "Thomas, Alan  and
      Gaizauskas, Robert  and
      Lu, Haiping",
    editor = "Sprugnoli, Rachele  and
      Passarotti, Marco",
    booktitle = "Proceedings of the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lt4hala-1.14",
    pages = "116--121",
    abstract = "Poor OCR quality continues to be a major obstacle for humanities scholars seeking to make use of digitised primary sources such as historical newspapers. Typical approaches to post-OCR correction employ sequence-to-sequence models for a neural machine translation task, mapping erroneous OCR texts to accurate reference texts. We shift our focus towards the adaptation of generative LLMs for a prompt-based approach. By instruction-tuning Llama 2 and comparing it to a fine-tuned BART on BLN600, a parallel corpus of 19th century British newspaper articles, we demonstrate the potential of a prompt-based approach in detecting and correcting OCR errors, even with limited training data. We achieve a significant enhancement in OCR quality with Llama 2 outperforming BART, achieving a 54.51{\%} reduction in the character error rate against BART{'}s 23.30{\%}. This paves the way for future work leveraging generative LLMs to improve the accessibility and unlock the full potential of historical texts for humanities research.",
}

Poor OCR quality continues to be a major obstacle for humanities scholars seeking to make use of digitised primary sources such as historical newspapers. Typical approaches to post-OCR correction employ sequence-to-sequence models for a neural machine translation task, mapping erroneous OCR texts to accurate reference texts. We shift our focus towards the adaptation of generative LLMs for a prompt-based approach. By instruction-tuning Llama 2 and comparing it to a fine-tuned BART on BLN600, a parallel corpus of 19th century British newspaper articles, we demonstrate the potential of a prompt-based approach in detecting and correcting OCR errors, even with limited training data. We achieve a significant enhancement in OCR quality with Llama 2 outperforming BART, achieving a 54.51{\%} reduction in the character error rate against BART{'}s 23.30{\%}. This paves the way for future work leveraging generative LLMs to improve the accessibility and unlock the full potential of historical texts for humanities research.
Thomas, Alan Gaizauskas, Robert Lu, Haiping
Leveraging LLMs for Post-OCR Correction of Historical Newspapers