---
license: cc0-1.0
language:
- vi
task_categories:
- image-to-text
tags:
- ocr
- vietnamese
- documents
- public-domain
- government-documents
- scanned-pdf
- contracts
- receipts
size_categories:
- n<1K
pretty_name: Vietnamese scanned document evaluation set (real PD + synthetic_scan)
configs:
- config_name: real
  data_files:
  - split: test
    path: real/**
- config_name: formal
  data_files:
  - split: test
    path: formal/**
- config_name: news_business
  data_files:
  - split: test
    path: news_business/**
- config_name: conversational
  data_files:
  - split: test
    path: conversational/**
- config_name: literary
  data_files:
  - split: test
    path: literary/**
- config_name: receipt
  data_files:
  - split: test
    path: receipt/**
- config_name: contract
  data_files:
  - split: test
    path: contract/**
- config_name: form
  data_files:
  - split: test
    path: form/**
---

# `vn-ocr-documents-eval` v0.3

107 single-page Vietnamese documents for evaluating PDF / image → DOCX
OCR pipelines. Six configs covering the full register matrix
(formal + business + conversational + literary) plus real PD scans
and synthetic receipts.

| Config | n | Source | License |
|---|---:|---|---|
| `real` | 9 | chinhphu.vn + hanoi.gov.vn signed scans | Public Domain (Luật SHTT VN, Điều 15) |
| `formal` | 24 | UDHR-vie articles + scan artifacts | CC0 (rendered) — UDHR text is PD |
| `news_business` | 24 | wiki_vi article openings + scan artifacts | CC-BY-SA 4.0 (Wikipedia VN) |
| `conversational` | 24 | tatoeba_vi sentence groups + scan artifacts | CC-BY 2.0 FR (Tatoeba) |
| `literary` | 23 | wikisource Truyện Kiều excerpts + scan artifacts | Public Domain |
| `receipt` | 21 | 7 templates x 3 seeds (tax invoice, payment, expense voucher, transport ticket, donation, utility, medical) | CC0 1.0 |
| `contract` | 20 | 5 templates x 4 seeds (labor, rental, economic, service, loan) | CC0 1.0 |
| `form` | 20 | 5 templates x 4 seeds (resignation, residence cert, enrollment, leave, business registration) | CC0 1.0 |

## What's new vs v0.1

- v0.1 was 100 % PIL-rendered synthetic content. That made
  `convert_to_docx` look like a 0.30 % CER tool — accurate for the
  synthetic, misleading for real scans.
- v0.2 ships **9 real public-domain Vietnamese government scans**
  across central + provincial domains and 7 document types
  (Quyết định, Công văn, Nghị quyết, Thông tư, Thông báo, Kế hoạch +
  the synthetic receipts).
- The honest baseline number on real scans is **12.62 %
  whitespace-normalized CER** — 42x worse than the v0.1 synthetic
  baseline. That's the gap a "real document OCR" pipeline actually
  needs to close.

## Source diversity

| Issuer | Source domain | Doc types |
|---|---|---|
| Thủ tướng Chính phủ | chinhphu.vn | Quyết định x 2 |
| Văn phòng Chính phủ | chinhphu.vn | Công văn |
| Chính phủ | chinhphu.vn | Nghị quyết |
| Bộ Công Thương | chinhphu.vn | Thông tư |
| Bộ Công an | chinhphu.vn | Thông tư |
| UBND TP Hà Nội | hanoi.gov.vn | Quyết định, Thông báo, Kế hoạch |
| (synthetic) | — | Hoá đơn, Biên lai, Phiếu chi |

## Usage

```python
from datasets import load_dataset

# Real PD scans (each row = one page + ground-truth text)
real = load_dataset("nrl-ai/vn-ocr-documents-eval", "real", split="test")
print(real[0]["text"][:120])
real[0]["image"].show()

# Synthetic receipts with scan artifacts
synth = load_dataset("nrl-ai/vn-ocr-documents-eval", "synthetic_scan", split="test")
```

For end-to-end PDF → DOCX evaluation, use the per-doc PDFs in
`docs/<doc_id>.pdf` plus the flat `metadata.jsonl` (`text` field) which
covers both configs.

## Reference baseline — `nom.convert.convert_to_docx`

Latest in-house bench
([source](https://github.com/nrl-ai/nom-vn/blob/main/benchmarks/results/baseline_convert_documents_v2.json)),
Tesseract 5 (`vie+eng` pack) via the `pdf_to_docx` OCR-fallback path:

| Config | CER (whitespace-normalized) | n |
|---|---:|---:|
| `real` | **12.62 %** | 9 |
| `synthetic_scan` | 0.43 % | 3 |
| **OVERALL** | **9.57 %** | 12 |

Throughput: ~0.7 docs/sec on a single CPU.

CER computed on whitespace-normalized strings (NFC, runs of whitespace
collapsed to single space). The 12.62 % real-config CER reflects the
combined difficulty of: (a) skewed scans with stamps, (b) admin
abbreviations Tesseract's vie pack doesn't handle gracefully
(signature blocks, "KT.", etc.), (c) low-contrast watermarks behind
body text.

Per-doc CER ranges 4-22 % — `real_qd_707_ttg` is the cleanest scan
(4 %), `real_hanoi_tb_453_flag` is the worst (22 %, short bold title
with stamps).

Run yourself:

```bash
git clone https://github.com/nrl-ai/nom-vn
cd nom-vn
pip install -e ".[doc]"
python benchmarks/data/vn_documents_ocr_v2/_generate.py
python benchmarks/accuracy/bench_convert_documents_v2.py
```

## Honesty notes

- **n=12 is small.** Use for smoke + regression checks; for adoption
  claims expand to 50-100 docs covering more issuers and date ranges.
- **Ground truth is human-verified visual transcription** — each
  `text` field was produced by directly reading the rendered page
  (not by trusting Tesseract output). Errors of judgment may remain
  in long numbered lists.
- **Page 1 only.** Some sources are multi-page; only page 1 is in
  the corpus. The `sources/<id>_full.pdf` files preserve the
  originals for future expansion.
- **No PII**. The chinhphu.vn / hanoi.gov.vn documents name public
  officials in their official capacity (PM, Ministers, Phó Chủ tịch
  UBND) — public record. Synthetic receipts contain only fictional
  names.

## License

- The 9 documents in `real` config: **Public Domain** under Luật Sở
  hữu trí tuệ Việt Nam, Điều 15 (Vietnamese government works are
  not subject to copyright). Source URLs in metadata.
- The 3 documents in `synthetic_scan` config: **CC0 1.0 Universal**
  (synthetic content, no rights reserved).
- This README + the `_generate.py` script: **CC0 1.0**.

## Citation

```bibtex
@dataset{nguyen_vn_ocr_documents_eval_v2_2026,
  author = {Nguyen, Viet-Anh and {Neural Research Lab}},
  title  = {{vn-ocr-documents-eval v0.2: Realistic Vietnamese
             scanned-document evaluation set with central + provincial
             government documents}},
  year   = {2026},
  url    = {https://huggingface.co/datasets/nrl-ai/vn-ocr-documents-eval}
}
```

Maintained as part of the [`nom-vn`](https://github.com/nrl-ai/nom-vn)
project by Viet-Anh Nguyen (`vietanh@nrl.ai`) and Neural Research Lab.