Infinity-Doc-55K
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💬 Web Demo
Overview
Infinity-Doc-55K is a high-quality diverse full-text parsing dataset, comprising 55K real-world and synthetic scanned documents. The dataset features rich layout variations and comprehensive structural annotations, enabling robust training of document parsing models. Additionally, this dataset encompasses a broad spectrum of document types, including financial reports, medical reports, academic reports, books, magazines, web pages, and synthetic documents.
Data Construction Pipeline
To construct a comprehensive dataset for document parsing, we integrate both real-world and synthetic data generation pipelines. Our real-world data pipeline collects diverse scanned documents from various practical domains (such as financial reports, medical records, and academic papers), employing a multi-expert strategy with cross-validation to generate reliable pseudo-ground-truth annotations for structural elements like text, tables, and formulas. Complementing this, our synthetic data pipeline programmatically creates a wide array of documents by injecting content from sources like Wikipedia into predefined HTML layouts, rendering them into scanned formats, and extracting precise ground-truth annotations directly from the original HTML. This dual approach yields a rich, diverse, and cost-effective dataset with accurate and well-aligned supervision, effectively overcoming common issues of imprecise or inconsistent labeling found in other datasets and enabling robust training for end-to-end document parsing models.
Data Statistics
| Document Type | Samples Number | Data Source |
|---|---|---|
| Synthetic Documents | 6.5k | CC3M + Web + Wiki |
| Financial Reports | 16.1k | Web |
| Medical Reports | 5k | Web |
| Academic Papers | 8.9k | Web |
| Books | 10.5k | Web |
| Magazines | 3k | Web |
| Web Pages | 5k | Web |
| All | 55k |
Data Structure
- id: The MD5 hash of the image, which serves as its unique identifier.
- image: The document image.
- gt: The content of the document, formatted in Markdown/HTML.
- attributes: Metadata describing the document type and task category.
Citation
@misc{wang2025infinityparserlayoutaware,
title={Infinity Parser: Layout Aware Reinforcement Learning for Scanned Document Parsing},
author={Baode Wang and Biao Wu and Weizhen Li and Meng Fang and Yanjie Liang and Zuming Huang and Haozhe Wang and Jun Huang and Ling Chen and Wei Chu and Yuan Qi},
year={2025},
eprint={2506.03197},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2506.03197},
}
License
This dataset is licensed under cc-by-nc-sa-4.0.