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--- |
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dataset_info: |
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features: |
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- name: ID |
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dtype: string |
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- name: Height |
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dtype: int64 |
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- name: Width |
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dtype: int64 |
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- name: Entity_Masks |
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dtype: string |
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- name: Grouped_OCR_Blocks |
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dtype: string |
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- name: Caption |
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dtype: string |
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- name: Tags |
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dtype: string |
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- name: OCR_Text |
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dtype: string |
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- name: URL |
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dtype: string |
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- name: URL_sha256 |
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dtype: string |
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- name: Language |
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dtype: string |
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- name: NSFW |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 1904454568 |
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num_examples: 120000 |
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download_size: 1144599398 |
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dataset_size: 1904454568 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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--- |
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# ADOPD: A Large-Scale Document Page Decomposition Dataset |
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[](.top_ADOPD_Tasks.png) |
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**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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--- |
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## 📊 Dataset Summary |
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- **Total Images**: 120,000 |
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- **Languages**: |
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- English: 60,000 |
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- Chinese: 20,000 |
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- Japanese: 20,000 |
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- Others: 20,000 |
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ADOPD ensures **diversity and balance** across document types and languages, validated through extensive experiments. |
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--- |
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## 🧩 Supported Tasks |
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| Task | Description | Field Name | |
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|------------|--------------------------------------------|----------------------| |
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| **Doc2Mask** | Segment entity regions as pixel-level masks | `Entity_Masks` | |
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| **Doc2Box** | Detect and group OCR text blocks | `Grouped_OCR_Blocks` | |
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| **Doc2Tag** | Predict high-level semantic tags | `Tags` | |
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| **Doc2Seq** | Generate abstracted captions | `Caption` | |
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--- |
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## 📝 Annotation Fields |
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Each data sample includes the following fields: |
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- `ID` / `URL_sha256`: Unique identifier for each document image |
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- `URL`: Direct link to download the image |
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- `Height`, `Width`: Image resolution in pixels |
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- `Entity_Masks`: Human-annotated segmentation masks for document entities |
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- `Grouped_OCR_Blocks`: Grouped OCR text blocks with bounding boxes |
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- `Caption`: Human-written descriptive caption summarizing the content |
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- `Tags`: Predicted document-level semantic tags |
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- `OCR_Text`: Raw plain-text extracted from the image |
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- `Language`: Language of the document content |
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- `NSFW`: Indicator flag for not-safe-for-work (NSFW) content |
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--- |
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## 📄 Citation |
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If you use ADOPD in your research, please cite: |
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```bibtex |
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@inproceedings{ |
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gu2024adopd, |
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title={{ADOPD}: A Large-Scale Document Page Decomposition Dataset}, |
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author={Jiuxiang Gu and Xiangxi Shi and Jason Kuen and Lu Qi and Ruiyi Zhang and Anqi Liu and Ani Nenkova and Tong Sun}, |
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booktitle={The Twelfth International Conference on Learning Representations}, |
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year={2024}, |
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url={https://openreview.net/forum?id=x1ptaXpOYa} |
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} |
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``` |
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--- |
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## 📜 License |
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- **License**: [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/) |
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- For **non-commercial use only**. Redistribution and derivative works are **not permitted**. |
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