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

## 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](https://huggingface.co/datasets/calfa-ai/RASAM-1) + [RASAM 2](https://huggingface.co/datasets/calfa-ai/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](https://github.com/calfa-co/tarima). ## 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 ```python 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](https://huggingface.co/datasets/calfa-ai/RASAM-1) and [original paper](https://link.springer.com/chapter/10.1007/978-3-030-86198-8_19) for details. ## Citation ```bibtex @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](https://www.apache.org/licenses/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.