Improving the quality of OCR transcriptions is crucial for researchers working with digital archives, as OCR errors hinder readability and downstream NLP tasks. While prior work has focused on seq2seq neural machine translation and, more recently, prompt-based approaches with large language models (LLMs), these methods rely solely on text, overlooking the visual characteristics of source documents. In this work, we explore the potential of vision-language models (VLMs) for post-OCR correction to evaluate the impact of integrating visual information. We conduct experiments on the BLN600 dataset of 19th-century newspaper articles, comparing Llama 3.2 Vision and Qwen 2.5 VL to their text-only LLM counterparts. Our results show that VLMs significantly outperform LLMs, achieving approximately 90% reduction in character and word error rates, with the optimal setting being one that takes as input both document image and prior noisy OCR. This suggests that VLM-based post-OCR correction can be a viable and effective approach for correcting back catalogues of poorly OCR’ed historical documents and improving the usability of digital archives.

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.

We present a publicly available corpus of nineteenth-century newspaper text focused on crime in London, derived from the Gale British Library Newspapers corpus parts 1 and 2. The corpus comprises 600 newspaper excerpts and for each excerpt contains the original source image, the machine transcription of that image as found in the BLN and a gold standard manual transcription that we have created. We envisage the corpus will be helpful for the training and development of OCR and post-OCR correction methodologies for historical newspaper machine transcription—for which there is currently a dearth of publicly available resources. In this paper, we discuss the rationale behind gathering such a corpus, the methodology used to select, process, and align the data, and the corpus’ potential utility for historians and digital humanities researchers—particularly within the realms of neural machine translation-based post-OCR correction approaches, and other natural language processing tasks that are critically affected by erroneous OCR.