GreenOCR-Dataset
Synthetic document pages for training OCR / document-parsing models, mainly Vietnamese with an English counterpart. Each page comes with
exact ground truth (block polygons and content) and a ready-to-train target in the LightOnOCR-3 grounding format. The pages are rendered,
not real scans; the augment parts are degraded variants (scan / phone / old-paper look).
Every type has three parts: vi and en (clean pages) and augment (degraded pages, Vietnamese and English mixed). slides has a fourth
part, bilingual: slides that mix Vietnamese and English text.
| Type | Pages vi / en / augment |
Total | What |
|---|---|---|---|
article |
11,153 / 4,152 / 6,054 | 21,359 | Magazine / newspaper pages |
paper |
7,979 / 3,890 / 4,241 | 16,110 | Scientific-paper pages |
legal |
8,214 / 3,694 / 4,297 | 16,205 | Legal-document pages |
wikimedia |
4,616 / 1,646 / 9,636 | 15,898 | Encyclopedia-style pages |
webcode |
3,241 / 1,637 / 10,500 | 15,378 | Rendered web pages |
standardbook |
4,357 / 3,058 / 13,673 | 21,088 | Book pages |
contract |
14,401 / 9,922 / 11,368 | 35,691 | Company contracts |
finreport |
9,903 / 8,756 / 10,000 | 28,659 | Financial statements and annual-report pages |
invoice |
19,590 / 8,220 / 6,983 | 34,793 | Invoices, receipts, stock slips, utility bills, plus handwritten vouchers and filled forms |
slides |
22,458 / 13,672 / 12,673, plus 15,011 bilingual |
63,814 | Presentation slides |
textbook |
18,077 / 6,818 / 11,740 | 36,635 | School textbook pages |
exam_paper |
13,710 / 6,830 / 5,622 | 26,162 | School exam papers, some filled in by hand; an option the student marked is written A*. in the target |
handwriting |
21,597 / 9,652 / 15,590 | 46,839 | Handwritten documents |
form |
10,425 / 7,272 / 12,737 | 30,434 | Forms filled in by hand; check boxes are written ☐ / ☑ |
handnotes |
4,497 / 3,561 / 9,815 | 17,873 | Handwritten notes |
426,938 pages in total.
Preview
The Dataset Viewer shows preview/<type>.parquet: about 100 example pages per type with their target (JPEG, long side up to 1400 px).
The pages are examples, not a random sample; the full set is in the parts below.
Layout
<type>/<part>/
images-000.tar, images-001.tar, ... page images (members "images/..."), PNG / JPG
gt.tar one .md file per page (the target, for reading)
shards.tar per-shard records (same schema as GroundTruth.jsonl)
GroundTruth.jsonl full record per page (polygons, blocks, target)
lighton.jsonl {"image_path", "target_lighton"}
Extract inside a part directory with for t in *.tar; do tar xf $t; done; image_path is then valid relative to that directory.
Ground truth
target_lighton is the page as LightOnOCR-3 grounding text, one block per paragraph in reading order:
 BÁO BÌNH MINH
 # Việt Nam là đối tác thương mại quan trọng nhất của Phần Lan ở Đông Nam Á
 Các cấp lãnh đạo Phần Lan ...
 gives the four corners of the block: top-left, top-right, bottom-right, bottom-left of the block as
printed, x normalised to the page width and y to the page height, 0..1000. On a tilted or rotated page the box turns with the text. A + after the label (text+) marks a block continued from a column or
page break. Content is Markdown, tables are HTML, formulas LaTeX; a chart is given as its data table, an image as a short description,
a diagram as Mermaid.
Labels: text, title, list, table, chart, image, formula, caption, header, footer, page_number, footnote, diagram.
Figure rules:
- chart: the plotted data as an HTML table, with the unit in the value headers.
- table: an HTML table.
- diagram: a
```mermaidblock that mermaid 11.4.1 parses. - image: a short English description. Visible text is quoted verbatim. No description is empty or a placeholder.
- formula:
$$ … $$LaTeX; inline math and sub/superscripts are$…$. A literal dollar sign is written$. - Decoration (icons, emoji, stickers) is not in the target.
A few images in the wikimedia image tars have no target and are not listed in lighton.jsonl.
Licence
Gated, for internal use. The licence terms will be added later.
Limitations
Pages are synthetic; do not use this set alone to measure accuracy on real documents. Strong augment settings can make small text partly
unreadable while the target still holds it. Some chart values and picture descriptions are estimates and can be wrong.
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