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        <title>Leveraging LLMs for Post-OCR Correction of Historical Newspapers</title>
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    <name type="personal">
        <namePart type="given">Alan</namePart>
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            <title>Proceedings of the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024</title>
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        <name type="personal">
            <namePart type="given">Rachele</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Leveraging LLMs for Post-OCR Correction of Historical Newspapers
%A Thomas, Alan
%A Gaizauskas, Robert
%A Lu, Haiping
%Y Sprugnoli, Rachele
%Y Passarotti, Marco
%S Proceedings of the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F thomas-etal-2024-leveraging
%X 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.
%U https://aclanthology.org/2024.lt4hala-1.14
%P 116-121

Markdown (Informal)

[Leveraging LLMs for Post-OCR Correction of Historical Newspapers](https://aclanthology.org/2024.lt4hala-1.14) (Thomas et al., LT4HALA-WS 2024)

• Leveraging LLMs for Post-OCR Correction of Historical Newspapers (Thomas et al., LT4HALA-WS 2024)

ACL

• Alan Thomas, Robert Gaizauskas, and Haiping Lu. 2024. Leveraging LLMs for Post-OCR Correction of Historical Newspapers. In Proceedings of the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024, pages 116–121, Torino, Italia. ELRA and ICCL.