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Text Region Detection in Historical Astronomical Diagrams

Official repository of the paper "Text region detection in historical astronomical diagrams". We introduce the first large, diverse, open-access dataset of 948 historical astronomical diagrams annotated with 10,940 oriented polygonal text regions that spans ten centuries (8th to 18th) and seven major traditions: Arabic, Persian, Chinese, Byzantine, Latin, Hebrew, and Sanskrit.

Dataset πŸ“œ

Content

We provide our dataset under two directories, namely EIDA and EIDALatin, with annotations in LabelMe format. EIDA contains images and associated annotations of all traditions (including Latin) under train, val, and test splits:

EIDA/
β”œβ”€β”€ train/
β”‚   β”œβ”€β”€ <filename_1>.jpg
β”‚   β”œβ”€β”€ <filename_1>.json
β”‚   β”‚   .
β”‚   β”‚   .
β”‚   β”œβ”€β”€ <filename_N>.jpg
β”‚   └── <filename_N>.json
β”œβ”€β”€ val/
└── test/

For class-aware annotations, we provide EIDALatin, which contains Latin subset with text classes, and splits in .txt format:

EIDALatin/
β”œβ”€β”€ data/
β”‚   β”œβ”€β”€ <filename_1>.jpg
β”‚   β”œβ”€β”€ <filename_1>.json
β”‚   β”‚   .
β”‚   β”‚   .
β”‚   β”œβ”€β”€ <filename_N>.jpg
β”‚   └── <filename_N>.json
β”œβ”€β”€ train.txt
β”œβ”€β”€ val.txt
└── test.txt

Citation πŸ”–

@inproceedings{baltaci2026text,
  title={Text region detection in historical astronomical diagrams},
  author={Baltaci, Zeynep Sonat and Baena, Rapha\"el and Meng, Fei and Norindr, Som and Somer, Florence and Husson, Matthieu and Aubry, Mathieu},
  booktitle={ICDAR},
  year={2026}
}

License

License: CC BY 4.0

This work is licensed under a Creative Commons Attribution 4.0 International License.

Acknowledgements

This work was funded by the ANR project EIDA ANR-22-CE38-0014, the ANR project VHS ANR-21-CE38-0008, and the ERC project DISCOVER funded by the European Union’s Horizon Europe Research and Innovation program under grant agreement No. 101076028. This work was granted access to the HPC resources of IDRIS under the allocation AD010614956R1 and AD011015222 made by GENCI. The authors would like to thank the many historians and computer vision researchers that contributed to the development of the dataset: Eleonora Andriani (Sphaera project, Max Planck Institute for the History of Sciences, Berlin), Ji Chen, Samuel Guessner, Divna Manolova, Scott Trigg (EIDA project), Malamatenia Vlachou Efstathiou, LΓ©ore Bensabath (ENPC), and Vidal Attias (CEA).

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