README.md

metadata

license: cc
task_categories:
  - object-detection
size_categories:
  - n<1K
configs:
  - config_name: EIDA
    data_files:
      - split: train
        path: hf_viewer/EIDA/train.parquet
      - split: validation
        path: hf_viewer/EIDA/val.parquet
      - split: test
        path: hf_viewer/EIDA/test.parquet
  - config_name: EIDALatin
    data_files:
      - split: train
        path: hf_viewer/EIDALatin/train.parquet
      - split: validation
        path: hf_viewer/EIDALatin/val.parquet
      - split: test
        path: hf_viewer/EIDALatin/test.parquet
dataset_info:
  features:
    - name: image
      dtype: image
    - name: file_name
      dtype: string
    - name: split
      dtype: string
    - name: image_width
      dtype: int32
    - name: image_height
      dtype: int32
    - name: shapes
      list:
        struct:
          - name: label
            dtype: string
          - name: shape_type
            dtype: string
          - name: points
            sequence:
              sequence: float32

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 (8^th to 18^th) and seven major traditions: Arabic, Persian, Chinese, Byzantine, Latin, Hebrew, and Sanskrit.

Dataset 📜

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/
├── train/
│   ├── <filename_1>.jpg
│   ├── <filename_1>.json
│   │   .
│   │   .
│   ├── <filename_N>.jpg
│   └── <filename_N>.json
├── val/
└── test/

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

EIDALatin/
├── data/
│   ├── <filename_1>.jpg
│   ├── <filename_1>.json
│   │   .
│   │   .
│   ├── <filename_N>.jpg
│   └── <filename_N>.json
├── train.txt
├── val.txt
└── test.txt

Citation 🔖

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

License

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, Léore Bensabath (ENPC), and Vidal Attias (CEA).