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

## 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](https://github.com/calfa-co/rasam-dataset). ## 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 ```python 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 ```bibtex @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](https://www.apache.org/licenses/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.