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---
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**.
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