pykale/bln600-img-patch

BLN600 Image Patches

This dataset provides BLN600's image patches for fine-tuning vision-language models on post-OCR correction, introduced in Image-Informed Post-OCR Correction with Vision-Language Models (EMNLP 2026 Findings). Each patch corresponds to a text sequence in BLN600 and is cropped from the Gale British Library Newspapers collection, using word-level bounding boxes from the collection's ALTO XML OCR layout data. It is intended to be used alongside the code and CSVs in Shef-AIRE/vlms_post-ocr_correction, which provide the corresponding OCR text, ground truth, metadata, and CER/WER for each patch.

Data Fields

Field	Type	Description
Image Patch	string	Filename, joins to the Image Patch column in the code repo's CSVs
image	image	The cropped newspaper-page region (TIFF)

License

Released under CC BY-NC-ND 4.0 (non-commercial, no derivatives). Permission was granted by Gale on behalf of the company and the British Library partners for non-commercial release, publicly accessible with no additional access stipulations.

Citation

@inproceedings{thomas-etal-2026-image,
    title = "Image-Informed Post-{OCR} Correction with Vision-Language Models",
    author = "Thomas, Alan  and
      Liu, Xianyuan  and
      Lu, Haiping  and
      Gaizauskas, Robert J.",
    editor = "Lee, Hung-Yi  and
      Dev, Sunipa  and
      Li, Jessy  and
      Elliott, Desmond",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.findings-emnlp.60/",
    pages = "1224--1237",
    ISBN = "979-8-89176-462-0",
    abstract = "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."
}