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README.md

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-license: cc-by-nc-nd-4.0
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+# Dataset Summary
+
+ADOPD is a large-scale dataset for document page decomposition, distinguished by a novel data-driven document taxonomy discovery method for data collection. This approach combines large-scale pretrained models with a human-in-the-loop process to ensure diversity and balance in the data. ADOPD includes densely annotated labels for document images, covering four tasks: Doc2Mask, Doc2Box, Doc2Tag, and Doc2Seq. Annotations for each image include human-labeled entity masks, text bounding boxes, and automatically generated tags and captions. Detailed experimental analyses validate the data-driven document taxonomy method and evaluate the four tasks using different models. ADOPD aims to support future research in document image understanding.
+
+# Dataset Information
+The ADOPD dataset contains a total of 120,000 images, with the following language distribution:
+- English: 60,000
+- Chinese: 20,000
+- Japanese: 20,000
+- Others: 20,000
+
+# Citation
+```
+@inproceedings{
+    gu2024adopd,
+    title={{AD}o{PD}: 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 (cc-by-nc-nd-4.0)
+