README.md

metadata

dataset_info:
  features:
    - name: ID
      dtype: string
    - name: Height
      dtype: int64
    - name: Width
      dtype: int64
    - name: Entity_Masks
      dtype: string
    - name: Grouped_OCR_Blocks
      dtype: string
    - name: Caption
      dtype: string
    - name: Tags
      dtype: string
    - name: OCR_Text
      dtype: string
    - name: URL
      dtype: string
    - name: URL_sha256
      dtype: string
    - name: Language
      dtype: string
    - name: NSFW
      dtype: string
  splits:
    - name: train
      num_bytes: 1904454568
      num_examples: 120000
  download_size: 1144599398
  dataset_size: 1904454568
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

ADOPD: A Large-Scale Document Page Decomposition Dataset

ADOPD is a large-scale dataset designed for document image understanding. It introduces a novel data-driven document taxonomy discovery framework that combines large-scale pretrained models with a human-in-the-loop refinement process. The dataset supports four core tasks and includes rich annotations to foster progress in document analysis.

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📊 Dataset Summary

• Total Images: 120,000
• Languages:
  • English: 60,000
  • Chinese: 20,000
  • Japanese: 20,000
  • Others: 20,000

ADOPD ensures diversity and balance across document types and languages, validated through extensive experiments.

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🧩 Supported Tasks

Task	Description	Field Name
Doc2Mask	Segment entity regions as pixel-level masks	Entity_Masks
Doc2Box	Detect and group OCR text blocks	Grouped_OCR_Blocks
Doc2Tag	Predict high-level semantic tags	Tags
Doc2Seq	Generate abstracted captions	Caption

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📝 Annotation Fields

Each data sample includes the following fields:

• ID / URL_sha256: Unique identifier for each document image
• URL: Direct link to download the image
• Height, Width: Image resolution in pixels
• Entity_Masks: Human-annotated segmentation masks for document entities
• Grouped_OCR_Blocks: Grouped OCR text blocks with bounding boxes
• Caption: Human-written descriptive caption summarizing the content
• Tags: Predicted document-level semantic tags
• OCR_Text: Raw plain-text extracted from the image
• Language: Language of the document content
• NSFW: Indicator flag for not-safe-for-work (NSFW) content

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📄 Citation

If you use ADOPD in your research, please cite:

@inproceedings{
    gu2024adopd,
    title={{ADOPD}: A Large-Scale Document Page Decomposition Dataset},
    author={Jiuxiang Gu and Xiangxi Shi and Jason Kuen and Lu Qi and Ruiyi Zhang and Anqi Liu and Ani Nenkova and Tong Sun},
    booktitle={The Twelfth International Conference on Learning Representations},
    year={2024},
    url={https://openreview.net/forum?id=x1ptaXpOYa}
}

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📜 License

• License: CC BY-NC-ND 4.0
• For non-commercial use only. Redistribution and derivative works are not permitted.