Datasets:

macpaw-research
/

Screen2AX-Tree

Screen2AX-Tree is part of the Screen2AX dataset suite, a research-driven collection for advancing accessibility in macOS applications using computer vision and deep learning.

This dataset provides hierarchical accessibility annotations of macOS application screenshots, structured as serialized trees. It is designed for training models that reconstruct accessibility hierarchies from visual input.

🧠 Dataset Summary

Each sample in the dataset consists of:

An application screenshot (image)
A serialized accessibility tree (accessibility): A JSON-formatted string representing the UI structure, including roles, bounds, and child relationships.

Task Category:

object-detection (structured / hierarchical)

Language:

English (en)

📚 Usage

Load with `datasets` library

from datasets import load_dataset

dataset = load_dataset("macpaw-research/Screen2AX-Tree")

Example structure

sample = dataset["train"][0]
print(sample.keys())
# dict_keys(['image', 'accessibility'])

print(sample["accessibility"])
# '{ "role": "AXWindow", "children": [ ... ] }'

You can parse the accessibility field as JSON to work with the structured hierarchy:

import json

tree = json.loads(sample["accessibility"])

📜 License

This dataset is licensed under the Apache 2.0 License.

🔗 Related Projects

✍️ Citation

If you use this dataset, please cite the Screen2AX paper:

@misc{muryn2025screen2axvisionbasedapproachautomatic,
      title={Screen2AX: Vision-Based Approach for Automatic macOS Accessibility Generation}, 
      author={Viktor Muryn and Marta Sumyk and Mariya Hirna and Sofiya Garkot and Maksym Shamrai},
      year={2025},
      eprint={2507.16704},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2507.16704}, 
}