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.- Anthology ID:
- 2024.lt4hala-1.14
- Volume:
- Proceedings of the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Rachele Sprugnoli, Marco Passarotti
- Venues:
- LT4HALA | WS
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 116–121
- Language:
- URL:
- https://aclanthology.org/2024.lt4hala-1.14
- DOI:
- Cite (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.
- Cite (Informal):
- Leveraging LLMs for Post-OCR Correction of Historical Newspapers (Thomas et al., LT4HALA-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lt4hala-1.14.pdf