README.md

---
pretty_name: "SEA-DocScene: A Multi-Scene and Multi-Category Real-World OCR Benchmark"
language:
  - km
  - lo
  - my
  - th
  - vi
tags:
  - ocr
  - document-layout-analysis
  - document-understanding
  - multilingual
  - coming-soon
task_categories:
  - image-to-text
---

# SEA-DocScene: A Multi-Scene and Multi-Category Real-World OCR Benchmark

> **Dataset status: Preview page — data files have not been released.**
> **Coming soon. No dataset files, samples, or downloadable archives are distributed through this repository at this stage.**

This dataset is developed and maintained by the **[Yunnan Key Laboratory of Artificial Intelligence]** at **[Kunming University of Science and Technology]**.

This repository currently serves as the official project and documentation page for the dataset.

## Overview

We present a document OCR and layout analysis benchmark covering five Southeast Asian languages: **Khmer (km)**, **Lao (lo)**, **Burmese/Myanmar (my)**, **Thai (th)** and **Vietnamese (vi)**. The benchmark is designed to evaluate text detection, text recognition and document layout analysis on real-world, in-the-wild documents from these low-resource script communities, spanning printed documents, scanned pages, presentation slides and photographic scene images.

Each page is annotated with page-level layout regions. Every region is described by a polygon, a layout category, a reading order and, for text-bearing regions, the transcript text plus line-level spans. Page metadata records image dimensions, the document source category, the language and the page layout style. The benchmark is intended to support both end-to-end OCR systems and document layout understanding models, and to expose the gap between high-resource and low-resource scripts.

Planned contents may include:

- Document page images (scanned pages, rendered documents and photographic scene images)
- Page-level layout annotations with polygons, categories and reading order
- Text transcripts at region level and line-span level
- A held-out benchmark split per language

## Languages Covered

| Code | Language | Script |
| ---- | -------- | ------ |
| `km` | Khmer | Khmer |
| `lo` | Lao | Lao |
| `my` | Burmese / Myanmar | Myanmar |
| `th` | Thai | Thai |
| `vi` | Vietnamese | Latin (with diacritics) |

## Document Categories

The benchmark covers 12 document source categories, each represented in every language:

| Category | English |
| -------- | ------- |
| 公文 | Official / administrative documents |
| 学术文献 | Academic literature |
| 研究报告 | Research reports |
| 书籍 | Books |
| 彩色教科书 | Color textbooks |
| 杂志 | Magazines |
| 报纸 | Newspapers |
| 试卷 | Exam papers |
| PPT转PDF | Presentation slides (PPT exported to PDF) |
| 笔记 | Handwritten notes and note pages |
| 证件照 | ID / document photos |
| 场景图像 | Scene images (documents captured in the wild) |

## Annotation Format

Annotations are provided as JSON, one record per page. Each page record contains:

- `layout_dets` — list of layout regions, each with a polygon `poly`, a `category_type`, a reading `order`, an `anno_id`, optional `text` and `line_with_spans` (line-level text with polygons), and `attribute` (text language, background, rotation)
- `page_info` — `page_no`, `page_code`, `document_code`, `image_path`, `width`, `height` and `page_attribute` (`data_source`, `language`, `layout`, `special_issue`)
- `extra` — relation metadata between regions

Layout categories include: `text_block`, `title`, `figure`, `table`, `header`, `footer`, `page_number`, `figure_caption`, `table_caption`, `equation_isolated`, `page_footnote`, `figure_footnote`, `table_footnote`, `equation_caption` and `reference`.

## Scale

The figures below reflect the dataset as currently prepared internally. Final released counts may change.

| Language | Pages | Document categories |
| -------- | ----- | ------------------- |
| `km` | 1,826 | 12 |
| `lo` | 2,016 | 12 |
| `my` | 1,844 | 12 |
| `th` | 1,755 | 12 |
| `vi` | 2,137 | 12 |
| **Total (benchmark split)** | **9,578** | — |

## Availability

The dataset is currently being prepared for release.


Researchers interested in the dataset, potential collaboration, or release notifications may contact:

- Laboratory: [Yunnan Key Laboratory of Artificial Intelligence]
- Institution: [Kunming University of Science and Technology ]
- Email: [maocunli@163.com]
- Principal investigator: [Cunli Mao]

When contacting us, please include your name, affiliation, intended research use, and the part of the dataset you are interested in.

## Access Policy

The final access mechanism and license are under review. Publication of this page does **not** grant access to the dataset or permission to use any underlying data.

The dataset may ultimately be released as:

- [Publicly downloadable / gated access / application-based access]
- [Research-only / non-commercial / another policy, if already approved]

Final terms will be published on this page before data files become available.

## Responsible Use

The dataset contains real documents collected from public and institutional sources and may include personal identifiers, official seals, signatures, contact details or other sensitive content. Some documents may reflect the views or biases of their original authors, which are not endorsed by the maintainers. Users are expected to avoid attempting to identify individuals, and to respect privacy and applicable data protection regulations.

Users will be expected to comply with the final license, applicable laws, ethical requirements, and any institutional restrictions described at release time.

## Citation

Citation information will be added when the accompanying paper or technical report becomes available.

## Version History

- **2026-09-18** — Initial preview page created; no data files released.