| --- |
| 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 |
| - config_name: AMIDDA |
| data_files: |
| - split: train |
| path: AMIDDA/data/train-*.parquet |
| - split: validation |
| path: AMIDDA/data/validation-*.parquet |
| - split: test |
| path: AMIDDA/data/test-*.parquet |
| - config_name: OpenITI-Makhzan_Metadata_2026-1-2 |
| data_files: |
| - split: train |
| path: OpenITI-Makhzan_Metadata_2026-1-2/train.csv |
| - split: validation |
| path: OpenITI-Makhzan_Metadata_2026-1-2/val.csv |
| - split: test |
| path: OpenITI-Makhzan_Metadata_2026-1-2/test.csv |
| --- |
| |
| # Arabic Manuscript Collection |
|
|
| Seven Arabic handwritten text recognition (HTR) subsets. Five are converted to **one layout** |
| and **one label format** so they can be trained and evaluated together: **82,561 labelled |
| images** in total. Two are republished closer to their source shape: AMIDDA as upstream |
| Parquet, and OpenITI-Makhzan as page images with line-level coordinates. |
|
|
| Four of the five converted sources are historical manuscripts. KHATT is modern handwriting |
| and is included as a separate, optional subset. |
|
|
| > **AMIDDA overlaps Muharaf and KHATT.** AMIDDA bundles its own copies of `muharaf-public` |
| > (24,494 lines) and KHATT v1.0 (6,673 lines), split independently of the `Muharaf` and |
| > `KHATT` subsets here. Training on AMIDDA and evaluating on `Muharaf`/`KHATT` test (or the |
| > reverse) leaks lines across the split boundary. Use one or the other, or filter AMIDDA on |
| > its `dataset` column. |
|
|
| 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)* | |
| | *AMIDDA* | *53,420* | *6,675* | *6,684* | *66,779* | *line* | *Upstream aggregate of 8 corpora: Maghrebi and Oriental Arabic manuscripts, lithography, modern cursive* | *mixed* | *Per source (see below)* | |
| | *OpenITI-Makhzan_Metadata_2026-1-2* | *18,447* | *2,343* | *2,289* | *23,079* | *line on a **page** image* | *208 documents, 1,497 pages. Multilingual: Arabic 12,773, Persian 5,381, Ottoman Turkish 3,654, Urdu 1,055. Six scripts, led by naskh and nastaliq* | *27%* | *Unverified (see below)* | |
|
|
| For the five converted subsets, every CSV row has a matching image file, and the train, |
| validation and test splits share no images. Two subsets differ: AMIDDA ships Parquet with |
| embedded images and no CSV, and `OpenITI-Makhzan_Metadata_2026-1-2` has many |
| rows per image, because its images are whole pages and each row is one line inside a page |
| (median 14 lines per page, up to 74). |
|
|
| **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 |
| ``` |
|
|
| `AMIDDA/` does not follow this layout. It is the upstream Parquet release, images embedded: |
|
|
| ``` |
| AMIDDA/ |
| data/train-*.parquet, validation-*.parquet, test-*.parquet |
| README.md # upstream card: per-corpus stats and baselines |
| ``` |
|
|
| `OpenITI-Makhzan_Metadata_2026-1-2/` keeps the `images.zip` + CSV shape but **not** the |
| two-column schema. Its 22 columns carry page-level line coordinates and catalogue metadata: |
|
|
| | Column group | Columns | |
| |---|---| |
| | Text | `text` | |
| | Image | `image_file` (a page, not a line crop), `page_id`, `page_number` | |
| | Geometry | `bbox_x`, `bbox_y`, `bbox_width`, `bbox_height`, `polygon` (all rows populated) | |
| | Provenance | `document_id`, `shelfmark`, `line_id`, `line_number`, `source_pdf` | |
| | Catalogue | `lang`, `script`, `genre`, `form`, `document_type` | |
| | QA | `transcriber`, `vetter`, `vetting_status` (every row vetted at least once) | |
|
|
| The geometry columns make this the only subset you can use for line segmentation or |
| layout analysis, not just recognition. To train a line recogniser on it, crop each line |
| from its page with `bbox_*` or `polygon` first. |
|
|
| - `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") |
| ``` |
|
|
| AMIDDA is Parquet, so the same call returns its images too, no zip handling needed: |
|
|
| ```python |
| ds = load_dataset("TheSeniorTeam/Arabic_Manuscript_Collection_Dataset", "AMIDDA") |
| ``` |
|
|
| `OpenITI-Makhzan_Metadata_2026-1-2` loads the same way as the CSV subsets, but |
| remember `image_file` names a page and the row is one line inside it: |
|
|
| ```python |
| ds = load_dataset("TheSeniorTeam/Arabic_Manuscript_Collection_Dataset", |
| "OpenITI-Makhzan_Metadata_2026-1-2") |
| arabic_naskh = ds["train"].filter(lambda x: x["lang"] == "ara" and x["script"] == "naskh") |
| ``` |
|
|
| **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 19.4 GB: AMIDDA 8.9 GB, OpenITI-Makhzan 6.1 GB, the rest 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 | |
| | AMIDDA | Upstream, unchanged | Upstream, unchanged | |
| | OpenITI-Makhzan_Metadata_2026-1-2 | As shipped, and **document-disjoint**: 98 train, 54 val, 56 test documents, no document or page in two splits | As shipped | |
|
|
| - **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. |
| - **`OpenITI-Makhzan_Metadata_2026-1-2` catalogue columns are not normalised.** `form` |
| mixes case and has at least one corrupt value (`prose`, `Prose`, `poetry`, `Poetry`, |
| `Prose, Poetry`, `ProseProse`, `calligraphed heading`). `source_pdf` is filled for only |
| 23% of rows. Normalise case before grouping on these. |
| - 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) | |
| | AMIDDA | `other`, and per-source on top: 8 upstream corpora with their own terms. Check before any redistribution or commercial use. | [`calfa-ai/AMIDDA`](https://huggingface.co/datasets/calfa-ai/AMIDDA) | |
| | OpenITI-Makhzan_Metadata_2026-1-2 | **Not yet verified.** The upstream terms for this release have not been confirmed, and the 208 documents come from multiple holding libraries with their own shelfmarks. Treat as all-rights-reserved until checked. | OpenITI / Makhzan (to be confirmed) | |
|
|
| ## 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. |
| - **AMIDDA:** Calfa, [`calfa-ai/AMIDDA`](https://huggingface.co/datasets/calfa-ai/AMIDDA). Cite the |
| eight upstream corpora listed on that card, not AMIDDA alone. |
| |
