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  # Overview
 
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  Infinity-Doc-55K is a high-quality diverse full-text parsing dataset, comprising 55,066 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.
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  ![Image](assets/dataset_illustration.png)
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  # Data Construction Pipeline
 
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  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.
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  ![Image](assets/data_construction_pipeline.png)
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  # Data Statistics
 
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  | Document Type | Samples Number |
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  | --- | --- |
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  | Synthetic Documents | 6546 |
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  | All | 55066 |
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  # Data Format
 
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  ```json
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  {
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  "images": ["path/to/image"],
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  ```
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  # License
 
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  This dataset is licensed under cc-by-nc-sa-4.0.
 
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+ # Infinity-Doc-55K
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+ <a><img src="assets/logo.png" height="16" width="16" style="display: inline"><b> Paper (coming soon) </b></a> |
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+ <a href="https://github.com/infly-ai/INF-MLLM/tree/main/Infinity-Parser"><img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" height="16" width="16" style="display: inline"><b> Github </b></a> |
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+ <a>💬<b> Web Demo (coming soon) </b></a>
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+
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  # Overview
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  Infinity-Doc-55K is a high-quality diverse full-text parsing dataset, comprising 55,066 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.
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  ![Image](assets/dataset_illustration.png)
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  # Data Construction Pipeline
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  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.
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  ![Image](assets/data_construction_pipeline.png)
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  # Data Statistics
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  | Document Type | Samples Number |
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  | --- | --- |
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  | Synthetic Documents | 6546 |
 
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  | All | 55066 |
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  # Data Format
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  ```json
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  {
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  "images": ["path/to/image"],
 
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  ```
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  # License
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  This dataset is licensed under cc-by-nc-sa-4.0.