Upload README.md

README.md

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+---
+pretty_name: Arabic Manuscript Collection
+language:
+- ar
+task_categories:
+- image-to-text
+tags:
+- ocr
+- htr
+- handwriting-recognition
+- arabic
+- manuscripts
+size_categories:
+- 10K<n<100K
+license: other
+license_name: mixed-per-subset
+configs:
+- config_name: Omar-al-Saleh
+  data_files:
+  - split: train
+    path: Omar-al-Saleh/train.csv
+  - split: validation
+    path: Omar-al-Saleh/val.csv
+  - split: test
+    path: Omar-al-Saleh/test.csv
+- config_name: Muharaf
+  data_files:
+  - split: train
+    path: Muharaf/train.csv
+  - split: validation
+    path: Muharaf/val.csv
+  - split: test
+    path: Muharaf/test.csv
+- config_name: AraMs-28k-HTR
+  data_files:
+  - split: train
+    path: AraMs-28k-HTR/train.csv
+  - split: validation
+    path: AraMs-28k-HTR/val.csv
+  - split: test
+    path: AraMs-28k-HTR/test.csv
+- config_name: AHDQ
+  data_files:
+  - split: train
+    path: AHDQ/train.csv
+  - split: validation
+    path: AHDQ/val.csv
+  - split: test
+    path: AHDQ/test.csv
+- config_name: KHATT
+  data_files:
+  - split: train
+    path: KHATT/train.csv
+  - split: validation
+    path: KHATT/val.csv
+  - split: test
+    path: KHATT/test.csv
+---
+
+# Arabic Manuscript Collection
+
+Five Arabic handwritten text recognition (HTR) datasets, converted to **one layout**
+and **one label format** so they can be trained and evaluated together:
+**82,561 labelled images** in total.
+
+Four of the sources are historical manuscripts. KHATT is modern handwriting and is
+included as a separate, optional subset.
+
+Curated by **TheSeniorTeam**, a PSU senior project supervised by Prof. Samer Samarah.
+We fine-tune an open vision-language model on historical Arabic handwriting and evaluate
+it with CER/WER on held-out manuscripts. This repository is that project's data module.
+
+## Subsets
+
+| Subset | Train | Val | Test | Total | Unit | Content | Diacritised lines | License |
+|---|---:|---:|---:|---:|---|---|---:|---|
+| **Omar-al-Saleh** | 15,164 | 798 | 2,095 | 18,057 | line | Handwritten memoirs, 1951–1965, 16 documents | ~3% | CC BY 4.0 |
+| **Muharaf** | 22,092 | 1,069 | 1,334 | 24,495 | line | Historic letters, journals, poetry, church and legal records | ~35% | CC BY-NC-SA 4.0 |
+| **AraMs-28k-HTR** | 19,739 | 1,486 | 6,744 | 27,969 | line | 14 books: 13 manuscripts in Naskh, Ruq'ah and Maghrebi scripts, plus 1 lithographed edition | 0% | CC BY-NC-SA 4.0 |
+| **AHDQ** | 579 | 31 | 32 | 642 | **page** | Binarized handwritten Quran pages, 6 handwriting styles | 100% | MIT |
+| *KHATT* | *8,094* | *1,901* | *1,403* | *11,398* | *line* | *Modern unconstrained handwriting, 838 writers* | *0%* | *Research use (see below)* |
+
+Every CSV row has a matching image file, and the train, validation and test splits share no images.
+
+**AHDQ is page-level.** Each image is a full page with a median of about 940 characters of
+text, up to 3,600. Every other subset uses line crops with a median of 47–70 characters.
+Batch AHDQ separately, or filter it out if your model has a short context.
+
+## Layout
+
+```
+<Subset>/
+  images.zip          # contains images/train/, images/val/, images/test/
+  train.csv           # image_path,text
+  val.csv
+  test.csv
+```
+
+- `image_path` is a bare filename inside `images/<split>/`. The CSV's name already
+  says which split it belongs to.
+- `text` is the ground-truth transcription in Unicode Arabic, not normalized. Diacritics
+  (tashkeel), punctuation and digits are kept exactly as the source provides them.
+- Images are PNG, except KHATT, which uses JPG.
+
+Extra files:
+
+| File | What it is |
+|---|---|
+| `AraMs-28k-HTR/metadata.csv` | Per-line provenance: `book_id`, `page_id`, `line_idx`, `line_type`, `split`. The `img_path` column predates the reorganisation, so join to the split CSVs on the filename. |
+| `AraMs-28k-HTR/split_used.json` | Which books are in which split. |
+| `AraMs-28k-HTR/dataset_stats.json` | Line, page and book counts per split and per book. |
+| `AHDQ/labels.csv` | The original unsplit label file for all 642 pages (`file_name,text`). |
+| `KHATT/khatt-label-decoder.ipynb` | Decodes KHATT's Latin transliteration codes into Arabic (see below). |
+
+## Usage
+
+**Text only**, for example to inspect labels or build a vocabulary:
+
+```python
+from datasets import load_dataset
+ds = load_dataset("TheSeniorTeam/Arabic_Manuscript_Collection_Dataset", "Muharaf")
+```
+
+**With images:**
+
+```python
+import zipfile
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+repo, subset, split = "TheSeniorTeam/Arabic_Manuscript_Collection_Dataset", "Muharaf", "train"
+
+zipfile.ZipFile(hf_hub_download(repo, f"{subset}/images.zip", repo_type="dataset")).extractall(subset)
+df = pd.read_csv(hf_hub_download(repo, f"{subset}/{split}.csv", repo_type="dataset"))
+df["image"] = f"{subset}/images/{split}/" + df["image_path"]
+```
+
+**Whole repo** (about 4.4 GB):
+
+```bash
+hf download TheSeniorTeam/Arabic_Manuscript_Collection_Dataset --repo-type dataset --local-dir .
+```
+
+## Splits: which are official and which we made
+
+**Do not re-split these subsets by line.** A line-level split puts the same writer or
+book in both train and test, so test scores measure memorisation instead of
+generalisation.
+
+| Subset | Train / test | Validation |
+|---|---|---|
+| Omar-al-Saleh | Official | **Ours.** 5% of official train, seed 0 |
+| Muharaf | Official | Official |
+| AraMs-28k-HTR | Official and **split by book**: 9 train, 2 val, 3 test books | Official |
+| AHDQ | **Ours.** No upstream split. 90/5/5, stratified by style so all six styles appear in every split | Ours |
+| KHATT | Train official. **Test is ours:** 104 of 691 train writers held out, seed 3, split by writer | Official, writer-disjoint from train |
+
+- **Omar-al-Saleh:** The 2,671-line **blind test** set from the NAKBA NLP 2026 shared task
+  is **deliberately not included**, so it can't end up in training by accident.
+  Upstream train has 15,969 rows. This copy has 15,962; the 7-row gap comes from upstream.
+- **AHDQ:** The style is encoded in the filename prefix: `dmn_`, `sdc_`, `sas_`, `sal_`,
+  `sbi_`, `sfm_`.
+- **KHATT:** `KHATT_v1.0` ships a test split but exposes no writer ID, so there is no way to
+  check that it shares no writers with this train set. We held out our own test set instead.
+
+## KHATT is optional
+
+KHATT is modern handwriting (1,000 writers originally, 838 in this copy), not manuscripts.
+It is here for pre-training or comparison. Leave it out if you are measuring manuscript HTR.
+
+The upstream CSVs label each line as Latin transliteration codes, one per column
+(`ke la sp ah na …`). We decoded them into Arabic with the Arabic-Latin lookup table in
+the KHATT v1.0 readme, plus 4 lam-alef ligature codes the table leaves out. All 67 codes
+resolve, covering 100% of tokens in both upstream CSVs, so no character was guessed.
+`khatt-label-decoder.ipynb` reproduces the decoding. Upstream CSVs name images `.tif`,
+but the files are `.jpg`; this copy uses the real filenames.
+
+## Known limitations
+
+- **No `source` or `document_id` column yet.** Document grouping is only partly available:
+  AraMs has `book_id` in `metadata.csv`, AHDQ has the style prefix, and Muharaf and this
+  Omar copy expose none. Until an ID column exists, you can't safely create a new split.
+- **Diacritics vary by subset**, from 0% (AraMs, KHATT) to 100% (AHDQ). A model trained on
+  a mix has to learn when tashkeel is expected. Report CER both with and without
+  diacritics.
+- A handful of Muharaf and Omar transcriptions contain a newline inside the text field,
+  and one Muharaf train row has empty text. Use a real CSV parser, not line splitting.
+- Omar-al-Saleh has a single writer, so good test scores there say little about other hands.
+
+## Licensing
+
+There is **no single license.** Each subset keeps its upstream terms, and using the
+collection as a whole means following all of them. The two CC BY-NC-SA subsets make any
+combined use **non-commercial**.
+
+| Subset | License | Upstream |
+|---|---|---|
+| Omar-al-Saleh | CC BY 4.0 | [`U4RASD/omar-al-saleh-manuscripts-segments`](https://huggingface.co/datasets/U4RASD/omar-al-saleh-manuscripts-segments) (Doha Institute) |
+| Muharaf | CC BY-NC-SA 4.0 | [`aamijar/muharaf-public`](https://huggingface.co/datasets/aamijar/muharaf-public) |
+| AraMs-28k-HTR | CC BY-NC-SA 4.0 | [Zenodo 10.5281/zenodo.22095333](https://doi.org/10.5281/zenodo.22095333) |
+| AHDQ | MIT | [Kaggle](https://www.kaggle.com/datasets/abdoukhadrembacke/ahdq-arabic-handwriting-dataset-from-quran) |
+| KHATT | Free for research, distributed by KFUPM. Check their terms before redistributing. | [KHATT, KFUPM](https://doi.org/10.1016/j.patcog.2013.08.009) |
+
+## Citation
+
+If you use a subset, cite its original authors:
+
+```bibtex
+@misc{saeed2024muharaf,
+  title         = {Muharaf: Manuscripts of Handwritten Arabic Dataset for Cursive Text Recognition},
+  author        = {Saeed, Mehreen and Chan, Adrian and Mijar, Anupam and Moukarzel, Joseph and Habchi, Georges and Younes, Carlos and Elias, Amin and Wong, Chau-Wai and Khater, Akram},
+  year          = {2024},
+  eprint        = {2406.09630},
+  archivePrefix = {arXiv},
+  note          = {NeurIPS 2024}
+}
+
+@misc{guechaoui2026arams28k,
+  title         = {AraMS-28k: The Largest Publicly Released Line-Level Dataset of Historical Arabic Manuscripts with Margin and Insertion-Anchor Annotations},
+  author        = {Guechaoui, Mohamed and Zellagui, Mohamed Diaa and Chaib, Souleyman and Dhelim, Sahraoui},
+  year          = {2026},
+  eprint        = {2608.26921},
+  archivePrefix = {arXiv}
+}
+
+@article{mahmoud2014khatt,
+  title   = {KHATT: An Open Arabic Offline Handwritten Text Database},
+  author  = {Mahmoud, Sabri A. and Ahmad, Irfan and Al-Khatib, Wasfi G. and Alshayeb, Mohammad and Parvez, Mohammad Tanvir and M{\"a}rgner, Volker and Fink, Gernot A.},
+  journal = {Pattern Recognition},
+  volume  = {47},
+  number  = {3},
+  pages   = {1096--1112},
+  year    = {2014},
+  doi     = {10.1016/j.patcog.2013.08.009}
+}
+```
+
+- **Omar-al-Saleh:** *NAKBA NLP 2026: Shared Task on Arabic Handwritten Manuscript
+  Understanding*, 2nd International Workshop, co-located with LREC 2026, Palma, Mallorca.
+- **AHDQ:** XIDMA_AI (2024). *AHDQ (Arabic Handwriting Dataset from Quran)*. Kaggle.