Document AMIDDA subset

README.md

@@ -55,16 +55,31 @@ configs:
     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
 ---
 
 # 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.
+Six Arabic handwritten text recognition (HTR) subsets. Five of them are converted to
+**one layout** and **one label format** so they can be trained and evaluated together:
+**82,561 labelled images** in total. The sixth, AMIDDA, is an upstream aggregate
+republished as-is in Parquet.
 
-Four of the sources are historical manuscripts. KHATT is modern handwriting and is
-included as a separate, optional subset.
+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
@@ -79,8 +94,10 @@ it with CER/WER on held-out manuscripts. This repository is that project's data
 | **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)* |
 
-Every CSV row has a matching image file, and the train, validation and test splits share no images.
+Every CSV row has a matching image file, and the train, validation and test splits share no
+images. AMIDDA is the exception: it ships Parquet with embedded images and no CSV.
 
 **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.
@@ -96,6 +113,14 @@ Batch AHDQ separately, or filter it out if your model has a short context.
   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
+```
+
 - `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
@@ -121,6 +146,12 @@ 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")
+```
+
 **With images:**
 
 ```python
@@ -135,7 +166,7 @@ df = pd.read_csv(hf_hub_download(repo, f"{subset}/{split}.csv", repo_type="datas
 df["image"] = f"{subset}/images/{split}/" + df["image_path"]
 ```
 
-**Whole repo** (about 4.4 GB):
+**Whole repo** (about 13.3 GB, of which AMIDDA is 8.9 GB):
 
 ```bash
 hf download TheSeniorTeam/Arabic_Manuscript_Collection_Dataset --repo-type dataset --local-dir .
@@ -154,6 +185,7 @@ generalisation.
 | 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 |
 
 - **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.
@@ -200,6 +232,7 @@ combined use **non-commercial**.
 | 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) |
 
 ## Citation
 
@@ -238,3 +271,5 @@ If you use a subset, cite its original authors:
 - **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.