| --- |
| pretty_name: VieDocOCR-Benchmark |
| license: other |
| license_name: mixed-per-subset |
| license_link: https://huggingface.co/datasets/GreenNode/VieDocOCR-Benchmark/blob/main/LICENSES.md |
| language: |
| - vi |
| task_categories: |
| - image-to-text |
| - image-text-to-text |
| tags: |
| - ocr |
| - document-parsing |
| - vietnamese |
| - benchmark |
| - handwriting |
| - tables |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # VieDocOCR-Benchmark |
|
|
| A benchmark for Vietnamese OCR and document parsing: **12,700 page images** in four subsets. Each subset has its own ground truth |
| format and its own metrics. **Report one number per subset; do not average across subsets.** |
|
|
| | Subset | Pages | What | Ground truth | Licence | |
| |---|---|---|---|---| |
| | `meddies` | 973 | Real Vietnamese administrative, financial and education pages: 390 text-only, 288 with tables, 295 with merged-cell tables | `meddies/data.jsonl` → `gt_html`: `<div data-bbox data-label>` blocks, tables as HTML with rowspan/colspan | source licence "other", see [LICENSES.md](LICENSES.md) | |
| | `handwriting` | 128 | Real handwritten paragraphs by 20 HANDS-VNOnDB writers | `handwriting/data.jsonl` → `text` | research only | |
| | `mdpvi` | 160 | The Vietnamese pages of MDPBench: 40 digital and 120 camera photos (textbooks, papers, slides, exams, ...) | `mdpvi/gt.json`, original MDPBench / OmniDocBench format | Apache-2.0 | |
| | `vidocparse` | 11,439 | **All** of ViDocParse (every split of the source repo): academic_paper 3204, financial_report 1962, newspaper 1233, textbook 1174, handwritten_note 1088, ppt_slide 861, exam_paper 768, magazine 605, book 544 | `vidocparse/src/<category>/<id>.json`, OmniDocBench format, unchanged | Apache-2.0 | |
| |
| Access is gated: the `meddies` and `handwriting` parts come from sources whose terms do not clearly allow open redistribution. Use the data |
| for research evaluation only. |
| |
| ## Files |
| |
| ``` |
| manifest.jsonl one row per page: sample_id, subset, image, gt (pointer), task, attrs |
| exclude_from_training.json every page id of this benchmark: keep them out of any training or tuning data |
| SHA256SUMS checksums of all data files |
| LICENSES.md licence and citation of each source |
| meddies/ data.jsonl images/ |
| handwriting/ data.jsonl images/ |
| mdpvi/ gt.json images/ |
| vidocparse/ src/<category>/<id>.png + <id>.json metadata/*.txt ViDocParse_test.json |
| ``` |
| |
| All paths in `manifest.jsonl` are relative to the repository root. Download the whole repo with |
| `huggingface-cli download GreenNode/VieDocOCR-Benchmark --repo-type dataset --local-dir VieDocOCR-Benchmark` |
| (about 64 GB, of which `vidocparse` is 63 GB). Use `--include "meddies/*" "handwriting/*" "mdpvi/*" "*.jsonl" "*.json"` for the |
| small subsets only (about 1.1 GB). |
|
|
| ### Ground-truth fields |
| - **meddies/data.jsonl**: `sample_id`, `image`, `stratum` (text-only / table / table-span), `gt_html` (the label to score against), |
| `original_html` (the label in the source dataset before correction, kept for comparison only), `review_verdict` (`ok` = source label was |
| already right, `edited` = corrected), `reviewer`, `review_note`, `unsure`, `tags`, `image_sha256`. Block labels: `Text`, `Section-Header`, `Table`, |
| `Page-Header`, `Page-Footer`, `List-Group`, `Image`, `Caption`, `Footnote`, `Equation-Block`, `Figure`, `Complex-Block` (bbox values are the source's boxes and were not re-checked). |
| - **handwriting/data.jsonl**: `sample_id`, `image`, `text` (paragraph transcription), `writer` (VNOnDB writer id). |
| - **mdpvi/gt.json**: the MDPBench annotation list for these 160 pages (`layout_dets`, `page_info`, `extra`). |
| - **vidocparse/src/<category>/<id>.json**: the source file as published: a one-element OmniDocBench list, except 140 files that hold the |
| page object itself (`{layout_dets, page_info, extra}`) without the list. |
| `vidocparse/metadata/*.txt` are the source's page-id lists (`test`, `train_manual`, `train_generated`, `train_mixed`, `manual`, |
| `generated`), re-saved with LF line endings; `manifest.jsonl` copies them into `attrs.source_split` (test / train) and |
| `attrs.annotation` (manual / generated). We have not verified how the `generated` annotations were produced; report |
| `manual` and `generated` pages separately if that distinction matters to you. |
| `vidocparse/ViDocParse_test.json` is the 1108 test pages in one OmniDocBench JSON with two fixes that the OmniDocBench scorer |
| needs: 547 header/footer/abandon items without text got `text: ""` and are ignored, and 15 `truncated` relations pointing at |
| those items were dropped. |
|
|
| ## How we score |
|
|
| Use the same prompt and settings for every subset, and give a bootstrap confidence interval over pages (we use 1000 resamples). |
|
|
| | Subset | Metrics | |
| |---|---| |
| | `meddies` | Page CER (normalised edit distance) with and without diacritics, diacritic-word error rate (share of GT words whose letters match but whose tone or vowel marks are wrong), TEDS and TEDS-structure-only on tables, loop rate and foreign-script (CJK) insertion rate | |
| | `handwriting` | CER, CER without diacritics, rate of pinyin or Chinese-character output, loop rate | |
| | `mdpvi` | the official MDPBench / OmniDocBench end-to-end metrics (text edit distance, table TEDS, reading order). They are comparable with published MDPBench numbers | |
| | `vidocparse` | OmniDocBench end-to-end metrics per category, plus page CER and the diacritic-word error rate on body text | |
|
|
| **Header / footer convention.** Meddies GT *includes* page headers, footers and page numbers. ViDocParse page text *excludes* them. |
| Models differ on whether they transcribe this page furniture, so for page CER we also report a body-only variant that removes GT |
| furniture blocks and drops the prediction lines that match them. Say which variant you report. |
|
|
| ## Caveats (read before quoting numbers) |
|
|
| - **Meddies labels were corrected by AI and have not been checked by a human.** The source labels (Meddies/meddies-ocr) are noisy: |
| roughly 1.8 errors per 100 words, and the table structure was wrong on 10 of 30 audited table pages. Every page was reviewed against |
| the image twice by Claude models (Opus, then Sonnet subagents). A person settled about 11 pages, and the two reviewers gave the same |
| verdict on 9 of 10 spot-checked pages. The residual error rate is unknown. |
| - Convention: labels copy the print, keeping typos, missing diacritics, Đ written as D, and number separators. Tables follow the image. |
| - Out of scope: stamps, signatures, text inside charts, and Arabic script. |
| - Excluded: pages that are not Vietnamese and pages whose font garbles diacritics (27 of 1000). |
| - Stamp text may remain on some pages reviewed early. |
| - **Meddies text is public.** These are real government and institutional documents, and about 150 of the 973 pages share most of |
| their text (≥50% of word 13-grams) with publicly available Vietnamese legal-document corpora. A model trained on such corpora may |
| have seen the text. A model trained on Meddies labels also learned the label conventions and gets an advantage on this subset, so |
| compare it on `mdpvi`, `handwriting` and `vidocparse` as well. |
| - **The handwriting images are pen strokes redrawn from online ink**, not scans or photos: 523 px wide on a white background. |
| Upscale them if your model needs larger input. The writers are held out from our own training data, but other VNOnDB writers |
| copied the same paragraph texts, so a model trained on VNOnDB text has seen most of these sentences. VNOnDB labels contain a few |
| errors. |
| - **MDP-VI is small** (40 digital, 120 photo pages), so treat small differences as noise. The MDPBench scorer at the time of writing |
| splits digital pages into pseudo-pages when it averages per page; regroup by page before you compute page averages. |
| - **ViDocParse is copied unchanged from an anonymous EMNLP 2026 submission** (`emnlp2026anon/ViDocParse`, Apache-2.0). In this |
| benchmark every split, including the source's train split, is evaluation data. Some scans are very large (up to about 140 MP), |
| so resize them before inference. |
| - Some pages may contain names of real people or organisations, because they are real public documents. If you find personal data |
| that should be removed, open a discussion on this repo. |
|
|
| ## Citation |
|
|
| If you use a subset, cite its source (see [LICENSES.md](LICENSES.md)): Meddies OCR, HANDS-VNOnDB, MDPBench, ViDocParse. |
|
|
