Datasets:

calfa-ai
/

RASAM-1

license: apache-2.0
language:
  - ar
task_categories:
  - image-to-text
tags:
  - htr
  - ocr
  - arabic
  - manuscripts
  - historical
size_categories:
  - 1K<n<10K
pretty_name: RASAM-1

RASAM 1 — Line-Level HTR Ground-Truth for Arabic historical manuscripts (Maghrebi)

Dataset Description

RASAM 1 is a specialized dataset for Handwritten Text Recognition (HTR) focusing on Arabic historical manuscripts in Maghrebi script.

This HuggingFace dataset provides cropped line-level images paired with their transcriptions and rich metadata for 3 Arabic Maghrebi manuscripts from the BULAC Library. It is designed as a ready-to-use resource for benchmarking and training HTR models on under-resourced historical scripts.


Manuscripts	3
Pages	299
Lines	7,331
Words	97,311
Characters	492,221

The full page-level dataset (PageXML + full-page images) is available on GitHub.

Source Documents

The corpus consists of three manuscripts from the BULAC Library (Paris), dating from the 17th to the 19th centuries:

BULAC_MS_ARA_417 (Historical)
BULAC_MS_ARA_609 (Inheritance law)
BULAC_MS_ARA_1977 (Historical)

Usage

from datasets import load_dataset

# Load the full dataset
ds = load_dataset("calfa-ai/rasam-1")

# Access a sample
sample = ds["train"][0]
sample["image"].show()
print(sample["transcription"])

# Filter by manuscript
bulac_609 = ds["train"].filter(lambda x: x["manuscript_name"].startswith("BULAC_MS_ARA_609"))

Transcription Guidelines (Summary)

The RASAM transcription protocol prioritizes fidelity to the scribe's practice over modern standard orthography to ensure robust HTR training. Key principles include:

Orthographic Preservation: Scribes' specific spellings (including archaisms and variant usages of characters like dād/ẓāʾ or ṣād/ṭāʾ) are maintained exactly as they appear in the manuscript.
Normalization Exceptions: To facilitate computational consistency, specific glyphic forms for relative pronouns (e.g., allaḏī/allatī) and the preposition fī are standardized.
Diacritics & Hamza: Vocalization and diacritics are generally excluded unless semantically essential. Hamzas are only transcribed if explicitly present in the source.
Handling Uncertainty: Illegible or damaged characters are marked with #. Gaps in the source text are not reconstructed.
Formatting: Spaces are restored in the transcription regardless of their visual presence in the manuscript layout.

Source: For a detailed breakdown of specifications and paleographical considerations, please refer to the full paper (see below)

Citation

@InProceedings{2021rasam-dataset,
  author    = {Vidal-Gorène, Chahan and Lucas, Noëmie and Salah, Clément and Decours-Perez, Aliénor and Dupin, Boris},
  editor    = {Barney Smith, Elisa H. and Pal, Umapada},
  title     = {RASAM -- A Dataset for the Recognition and Analysis of Scripts in Arabic Maghrebi},
  booktitle = {Document Analysis and Recognition -- ICDAR 2021 Workshops},
  year      = {2021},
  publisher = {Springer International Publishing},
  address   = {Cham},
  pages     = {265--281},
  isbn      = {978-3-030-86198-8}
}

License

This dataset is released under the Apache License 2.0.

Acknowledgements

The RASAM dataset was developed collaboratively between 2021–2023 by the Research Consortium Middle-East and Muslim Worlds (GIS MOMM), DISTAM, Calfa, and the BULAC Library. The project was funded by the French Ministry of Higher Education, Research and Innovation.