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Khmer Document Corpus
Overview
Khmer Document Corpus is an open-source dataset designed for Document AI research, focusing on Khmer, English, and multilingual Khmer-English documents.
The long-term goal of this project is to build one of the largest open document corpora for Khmer, supporting research in document understanding, OCR, document layout analysis, table extraction, font recognition, and PDF-to-Word reconstruction.
Version v0.1 is the initial release containing raw PDF documents and metadata.
Objectives
This dataset is being developed to support future research in:
- Document AI
- Optical Character Recognition (OCR)
- Khmer OCR
- English OCR
- Multilingual OCR
- Document Layout Analysis
- Table Recognition
- Reading Order Detection
- Font Recognition
- PDF Parsing
- PDF-to-Word Conversion
- Vision-Language Models (VLM)
- Information Extraction
- Large Multimodal Models (LMM)
Languages
The dataset currently focuses on:
- Khmer (
km) - English (
en) - Mixed Khmer-English
Future versions may include additional Southeast Asian languages.
Document Types
The corpus will gradually include publicly available documents such as:
- Government Reports
- Government Forms
- Laws
- Gazettes
- Books
- Research Papers
- Manuals
- Annual Reports
- Financial Reports
- Certificates
- Contracts
- Invoices
- Receipts
- Newspapers
- Magazines
- Presentations
Current Release
Version
v0.1
Current contents:
- Raw PDF documents
- Basic document metadata
Future releases will include:
- Page preview images
- OCR annotations
- Layout annotations
- Table annotations
- Font annotations
- Reading order annotations
- Ground truth DOCX files
- Structured JSON annotations
Dataset Structure
khmer-document-corpus/
├── metadata/
├── pdf/
├── preview/
├── scripts/
└── README.md
Metadata
Each document may contain the following metadata.
| Field | Description |
|---|---|
| id | Unique document ID |
| language | Document language |
| category | Document category |
| pages | Number of pages |
| native_pdf | Whether text is embedded |
| scanned | Whether OCR is required |
| has_tables | Contains tables |
| has_images | Contains images |
| has_header | Header detected |
| has_footer | Footer detected |
| source | Collection source |
| license | Original document license |
Intended Use
This dataset is intended for:
- Academic research
- OCR benchmarking
- Document AI
- Computer Vision
- Natural Language Processing
- Vision-Language Models
- PDF processing
- Information Extraction
- Machine Learning
Data Collection
Documents are collected from publicly available sources or documents that can be legally redistributed.
Potential sources include:
- Government publications
- Universities
- Public reports
- Research publications
- Open-access books
- Public forms
- Public documentation
Documents with unclear redistribution rights should not be included.
Licensing
This repository is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).
The repository license applies to the dataset organization and metadata.
Individual documents may have their own licenses or terms of use. Users are responsible for complying with the license associated with each original document.
Limitations
Current limitations include:
- Small initial dataset
- No OCR annotations
- No layout annotations
- No font annotations
- No table annotations
- No reading-order annotations
These will be added in future releases.
Roadmap
| Version | Status | Description |
|---|---|---|
| v0.1 | ✅ | Raw PDF Corpus |
| v0.2 | ⬜ | Metadata Expansion |
| v0.3 | ⬜ | Page Preview Images |
| v0.4 | ⬜ | OCR Ground Truth |
| v0.5 | ⬜ | Layout Annotations |
| v0.6 | ⬜ | Table Annotations |
| v0.7 | ⬜ | Font Annotations |
| v0.8 | ⬜ | Reading Order |
| v1.0 | ⬜ | PDF-to-Word Benchmark |
Citation
If you use this dataset in your research, please cite:
@dataset{khmer_document_corpus_2026,
title={Khmer Document Corpus},
author={Darachhat},
year={2026},
publisher={Hugging Face},
url={https://huggingface.co/datasets/Darachhat/khmer-document-corpus}
}
Contributing
Contributions are welcome.
Future contributions may include:
- Public PDF collections
- Metadata improvements
- OCR annotations
- Layout annotations
- Table annotations
- Font annotations
- Documentation improvements
Please ensure that all contributed documents can be legally redistributed before submitting a pull request.
Contact
Maintainer: Darachhat
For questions, suggestions, or collaboration, please open an issue in the repository.
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