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README.md

@@ -1,40 +1,108 @@
 ---
 license: apache-2.0
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: transcription
-    dtype: string
-  - name: support_type
-    dtype: string
-  - name: region_type
-    dtype: string
-  - name: manuscript_name
-    dtype: string
-  - name: image_name
-    dtype: string
-  - name: script
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 2461263786
-    num_examples: 5864
-  - name: validation
-    num_bytes: 307657973
-    num_examples: 733
-  - name: test
-    num_bytes: 308077697
-    num_examples: 734
-  download_size: 3076235636
-  dataset_size: 3076999456
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
+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)
+
+<p align="center">
+  <a href="https://doi.org/10.1007/978-3-030-86198-8_19"><img src="https://img.shields.io/badge/Paper-ICDAR 2021-blue" alt="Paper ICDAR 2021"></a>
+  <a href="https://github.com/calfa-co/rasam-dataset"><img src="https://img.shields.io/badge/GitHub-PageXML dataset-green" alt="GitHub"></a>
+  <a href="https://calfa.fr"><img src="https://img.shields.io/badge/Platform-Calfa Vision-purple" alt="Calfa"></a>
+</p>
+
+## 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.
+
+<div style="display: flex; align-items: center; gap: 15px;">
+  <img src="./assets/bulac.png" style="height: 50px;" alt="bulac-logo">
+  <img src="./assets/calfa.png" style="height: 50px;" alt="calfa-logo">
+  <img src="./assets/distam.png" style="height: 50px;" alt="distam-logo">
+  <img src="./assets/gis-momm.png" style="height: 50px;" alt="gis-logo">
+  <img src="./assets/cnrs.png" style="height: 50px;" alt="cnrs-logo">
+  <img src="./assets/mesri.png" style="height: 50px;" alt="mesri-logo">
+</div>