maximazzik/MAS

MAS: A Millennium of Arabic Manuscripts in Three Styles

MAS (Medieval Arabic Script) is a line-level OCR benchmark of 11,841 expert-annotated lines from nine authentic Arabic manuscript works. It covers Naskh, Taliq, and Nastaliq. Copies date from the 12th to the 20th century; compositions from the 10th to the early 20th.

This dataset accompanies the ICDAR 2026 paper *A Millennium of Arabic Manuscripts in Three Styles: A Line-Level OCR Benchmark for Naskh, Taliq, and Nastaliq*.

Training configurations, the lmms-eval task, and evaluation notes are in the MAS GitHub repository.

<p align="center"> <img src="assets/calligraphy-styles.jpg" alt="Naskh, Taliq, and Nastaliq page examples from MAS" width="900"> </p> <p align="center"><em>Calligraphic styles in MAS, left to right: Naskh, Taliq, Nastaliq.</em></p>

Splits

The dataset exposes one configuration, mas:

The split is 80/20 at image level with no page overlap. All nine works occur in both splits, so this is not a manuscript-held-out evaluation. The published test split is the held-out evaluation set used in the paper.

from datasets import load_dataset

dataset = load_dataset("maximazzik/MAS", "mas")
print(dataset)
print(dataset["train"][0].keys())

Coverage

The paper groups the collection into five domains: astronomy, history, mathematics, religion, and Sufi literature.

The domain field uses six machine-readable labels. siyasat (advice to rulers) and prayers together correspond to the paper's Religion group:

Record schema

Annotation

Transcriptions are diplomatic: original spelling, punctuation, dots, diacritics, and historical forms are preserved without modernization or correction. Each line was independently transcribed by two trained researchers, with disagreements resolved through cross-validation and discussion.

Evaluation

Open-source LVLMs were evaluated with `lmms-eval`. Specialized OCR systems and closed-source models were scored from saved predictions using the same CER/WER definitions. See `docs/reproduction.md` and `docs/results.md`.

Citation

If you use the MAS dataset, please cite:

@inproceedings{novopoltsev2027mas,
  title={A Millennium of Arabic Manuscripts in Three Styles: A Line-Level OCR Benchmark for Naskh, Taliq, and Nastaliq},
  author={Novopoltsev, Maxim and Murtazin, Ruslan and Sakhovskiy, Andrey and Bojarskaja, Emilia and Kokh, Vladimir and Ulitin, Ivan and Abdullayev, Botirjon and Aminov, Khamidulla and Ismoilov, Masudkhon and Budennyy, Semen},
  booktitle={Document Analysis and Recognition -- ICDAR 2026},
  pages={643--659},
  year={2027},
  publisher={Springer Nature Switzerland},
  doi={10.1007/978-3-032-36033-5_38}
}