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

Dataset Description

TariMa is a specialized dataset designed to support Handwritten Text Recognition (HTR) and Optical Character Recognition (OCR) for historical Maghrebi Arabic documents. This dataset serves as a complementary resource to the RASAM (RASAM 1 + RASAM 2) dataset, focusing on specific vocabularies and document types—such as chronicles, travelogues, and biographical dictionaries—that were previously underrepresented. Images vary significantly in resolution (982 px to 8,049 px width), layout complexity (single/multiple columns), and preservation state.

This HuggingFace dataset provides cropped line-level images paired with their transcriptions and rich metadata for 26 Maghrebi Arabic documents — both manuscripts and lithographed editions — including historical chronicles, travelogues, and biographical dictionaries. It is designed as a ready-to-use resource for Arabic Maghrebi HTR and OCR.

Documents	26
Pages	103
Lines	2,285
Words	26,469
Characters	138,001

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

Source Documents

Documents from three French institutions, spanning both manuscripts and lithographed editions:

• BULAC — Bibliothèque universitaire des langues et civilisations (Paris)
  • Manuscripts: BULAC_MS_ARA_410, BULAC_MS_ARA_427, BULAC_MS_ARA_436C, BULAC_MS_ARA_453, BULAC_MS_ARA_794
  • Lithographies: BULAC_RES_MON_4_3416, BULAC_RES_MON_4_3423, BULAC_RES_MON_4_3424, BULAC_RES_MON_8_3955, BULAC_RES_MON_8_4352, BULAC_RES_MON_8_4734, BULAC_RES_MON_8_5336, BULAC_RES_MON_8_5560, BULAC_RES_MON_8_6212, BULAC_RES_MON_8_7199, BULAC_RES_MON_8_8967
• BnF — Bibliothèque nationale de France (Paris)
  • BnF-2297_btv1b11001977p, BnF-4617_btv1b10030580s, BnF-6898_btv1b10030178f, BnF-7024_btv1b10031132f, BnF-7214_btv1b10031231c
• MMSH / IREMAM — Maison Méditerranéenne des Sciences de l'Homme (Aix-en-Provence)
  • MMSH_IREMAM_ARA_MS_011, MMSH_MS_050, MMSH_MS_051_1, MMSH_MS_051_2, MMSH_MS_056

Usage

from datasets import load_dataset

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

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

# Filter by support type
manuscripts    = ds["train"].filter(lambda x: x["support_type"] == "manuscript")
lithographies  = ds["train"].filter(lambda x: x["support_type"] == "lithography")

Transcription Guidelines (Summary)

The transcription guidelines follow the RASAM specification: transcriptions preserve the text as found in the image, including variant spellings, while excluding vocalization marks unless structurally significant. See RASAM 1 and original paper for details.

Citation

@inproceedings{2024rasam-dataset,
  title     = {{Enhancing Arabic Maghribi Handwritten Text Recognition with RASAM 2: A Comprehensive Dataset and Benchmarking}},
  author    = {Vidal-Gorène, Chahan and Salah, Clément and Lucas, Noëmie and Decours-Perez, Aliénor and Perrier, Antoine},
  url       = {https://enc.hal.science/hal-04722622},
  booktitle = {{Computational Humanities Research (CHR)}},
  address   = {Aarhus, Denmark},
  volume    = {3834},
  pages     = {200--216},
  year      = {2024},
  month     = dec,
}

License

This dataset is released under the Apache License 2.0.

Acknowledgements

The TariMa dataset was developed through the TariMa project (Tārīḫ al-Maġrib), funded by the French agency CollEx-Persée. The project was conducted under the supervision of Antoine Perrier (CNRS) in collaboration with the BULAC, the Maison Méditerranéenne des Sciences de l'Homme, IREMAM, and Calfa.