| | --- |
| | 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 |
| |
|
| | [](.top_ADOPD_Tasks.png) |
| |
|
| | **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. |
| |
|
| | --- |
| |
|
| | ## 📊 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. |
| |
|
| | --- |
| |
|
| | ## 🧩 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` | |
| |
|
| | --- |
| |
|
| | ## 📝 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 |
| |
|
| | --- |
| |
|
| | ## 📄 Citation |
| |
|
| | If you use ADOPD in your research, please cite: |
| |
|
| | ```bibtex |
| | @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} |
| | } |
| | ``` |
| |
|
| | --- |
| |
|
| | ## 📜 License |
| |
|
| | - **License**: [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/) |
| | - For **non-commercial use only**. Redistribution and derivative works are **not permitted**. |
| |
|
