README.md

---
language:
- ar
pretty_name: MAS
task_categories:
- image-to-text
tags:
- optical-character-recognition
- handwritten-text-recognition
- historical-documents
- arabic
- naskh
- taliq
- nastaliq
license: other
size_categories:
- 10K<n<100K
configs:
- config_name: mas
  data_files:
  - split: train
    path: mas/train-*
  - split: test
    path: mas/test-*
---

# 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*](https://link.springer.com/chapter/10.1007/978-3-032-36033-5_38).

Training configurations, the `lmms-eval` task, and evaluation notes are in the
[MAS GitHub repository](https://github.com/ai-forever/MAS).

<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`:

| Split | Lines | Share |
|---|---:|---:|
| `train` | 9,472 | 80% |
| `test` | 2,369 | 20% |

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.

```python
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:

| Paper domain | `domain` | `domain_label` |
|---|---|---|
| Astronomy | `astronomy` | Astronomy |
| History | `history` | History |
| Mathematics | `mathematics` | Mathematics |
| Religion | `siyasat` | Advice to rulers (siyasat-nama) |
| Religion | `prayers` | Prayer collection |
| Sufi literature | `sufi_lit` | Sufi literature |

## Record schema

| Field | Meaning |
|---|---|
| `image` | Embedded line image |
| `text` | Diplomatic UTF-8 transcription |
| `prompt` | OCR instruction used to construct the sample |
| `messages` | ShareGPT-style user/assistant messages |
| `script` | Naskh, Taliq, or Nastaliq |
| `domain`, `domain_label` | Machine-readable and display domain names |
| `work`, `author`, `author_dates` | Bibliographic metadata |
| `composition_date`, `copy_date` | Work and manuscript chronology |
| `manuscript_id` | Stable identifier for one of the nine works |
| `source_pack`, `line_id` | Source grouping and line-level traceability |
| `split` | `train` or `test` |

## 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`](https://github.com/EvolvingLMMs-Lab/lmms-eval). Specialized OCR
systems and closed-source models were scored from saved predictions using the
same CER/WER definitions. See
[`docs/reproduction.md`](https://github.com/ai-forever/MAS/blob/main/docs/reproduction.md)
and [`docs/results.md`](https://github.com/ai-forever/MAS/blob/main/docs/results.md).

## Citation

If you use the MAS dataset, please cite:

```bibtex
@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}
}
```