Datasets:

davanstrien
/

ncse-v2

license: cc-by-4.0
language:
  - en
task_categories:
  - text-generation
  - image-to-text
tags:
  - newspapers
  - historical
  - ocr
  - glam
  - nineteenth-century
size_categories:
  - 1M<n<10M
configs:
  - config_name: default
    data_files: data/*.parquet

NCSE v2.0 — OCR-processed 19th-century English newspapers (working mirror)

Private working mirror, not an original work. Source: Jonno Bourne, NCSE v2.0: A Dataset of OCR-Processed 19th Century English Newspapers, UCL Research Data Repository, 2025. doi:10.5522/04/28381610.v1 — CC BY 4.0. Mirrored here for analysis convenience (parquet-native loading, Dataset Viewer). All credit to the original author.

The Nineteenth Century Serials Edition re-OCR'd with Pixtral 12B, reported at 1% median character error rate — 5× lower than the next best approach (paper, arXiv 2502.14901, code).

1,378,882 text segments across six periodicals, 1806–1890.

periodical	segments
Northern Star	904,516
Leader	271,082
Publishers' Circular	98,674
Monthly Repository	79,606
English Woman's Journal	13,350
Tomahawk	11,654

path	what
`data/`	main OCR text, one parquet per periodical (default config)
`ground_truth/`	358 human ground-truth `.txt` files, one per text region
`cropped_images/`	378 region crops (`.png`) pairing with the ground truth by region id
`bounding_boxes/`	per-region layout coordinates
`classification/`	IPTC topic + text-type classifications per segment
`silver_labels/`	silver IPTC / text-type label parquet
`class_mappings/`	label id → name JSON (18 IPTC topics, 4 text types)
`original_readme.txt`	the author's field descriptions, verbatim

Main schema: issue_id · page_number · block · column · class · reading_order · content · segment_count · prompt_tokens · completion_tokens · total_tokens · box_page_id · page_id

⚠️ Verified structure notes (checked 2026-08-05, not in the original docs)

ground_truth/ pairs with cropped_images/, NOT with data/. All 358 GT files have a matching crop ({issue}_page_{n}_{box}; the GT filename adds a _box_page_id_ infix). Together they are a standalone OCR eval set: run a model on the crop, score against the transcription.
The GT does not join cleanly to the released text. Only 107 of 358 GT region ids exist in data/, and region numbering diverges (e.g. CLD-1852-04-24_page_6 jumps R7 → R10). So the paper's 1% CER headline cannot be reproduced from this release by comparing published OCR against published GT — the two were evidently produced from different segmentation runs. This is a documentation/alignment gap, not evidence that the OCR is poor.
GT text preserves original line breaks and end-of-line hyphenation (perma-\nnent); data/ content is post-processed and de-hyphenated. Normalise before scoring.

Why mirrored

A rare high-quality historic-newspaper corpus — most heritage OCR is far worse than 1% CER, so this is a useful clean reference point against noisy corpora.
ground_truth/ + cropped_images/ form a real paired eval set (image crop ↔ human transcription) for 19th-c English newsprint — scarce, and directly usable for calibrating OCR-quality metrics.
It was published only to a university repository with zero Hub presence, so it is invisible to the ML ecosystem despite being CC BY.

Original page images are held by King's College London (Monthly Repository, Northern Star, Leader, English Woman's Journal, Tomahawk, Publishers' Circular) and are not included here.

Citation

@misc{bourne_ncse_v2,
  author    = {Bourne, Jonno},
  title     = {{NCSE v2.0}: A Dataset of {OCR}-Processed 19th Century English Newspapers},
  year      = {2025},
  publisher = {UCL Research Data Repository},
  doi       = {10.5522/04/28381610.v1}
}

NCSE v2.0 — OCR-processed 19th-century English newspapers (working mirror)

Contents

⚠️ Verified structure notes (checked 2026-08-05, not in the original docs)

Why mirrored

Citation