---
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)

> [!NOTE]
> **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](https://doi.org/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](https://doi.org/10.1093/llc/fqaf151), [arXiv 2502.14901](https://arxiv.org/abs/2502.14901), [code](https://github.com/JonnoB/reading_the_unreadable)).

**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 |

## Contents

| 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

1. **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.
2. **`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.
3. 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

```bibtex
@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}
}
```