Document OpenITI-Makhzan subset; restore standard LFS .gitattributes

.gitattributes

@@ -1,22 +1,44 @@
-AMIDDA/data/test-00000-of-00002.parquet filter=lfs diff=lfs merge=lfs -text
-AMIDDA/data/test-00001-of-00002.parquet filter=lfs diff=lfs merge=lfs -text
-AMIDDA/data/train-00000-of-00016.parquet filter=lfs diff=lfs merge=lfs -text
-AMIDDA/data/train-00001-of-00016.parquet filter=lfs diff=lfs merge=lfs -text
-AMIDDA/data/train-00002-of-00016.parquet filter=lfs diff=lfs merge=lfs -text
-AMIDDA/data/train-00003-of-00016.parquet filter=lfs diff=lfs merge=lfs -text
-AMIDDA/data/train-00004-of-00016.parquet filter=lfs diff=lfs merge=lfs -text
-AMIDDA/data/train-00005-of-00016.parquet filter=lfs diff=lfs merge=lfs -text
-AMIDDA/data/train-00006-of-00016.parquet filter=lfs diff=lfs merge=lfs -text
-AMIDDA/data/train-00007-of-00016.parquet filter=lfs diff=lfs merge=lfs -text
-AMIDDA/data/train-00008-of-00016.parquet filter=lfs diff=lfs merge=lfs -text
-AMIDDA/data/train-00009-of-00016.parquet filter=lfs diff=lfs merge=lfs -text
-AMIDDA/data/train-00010-of-00016.parquet filter=lfs diff=lfs merge=lfs -text
-AMIDDA/data/train-00011-of-00016.parquet filter=lfs diff=lfs merge=lfs -text
-AMIDDA/data/train-00012-of-00016.parquet filter=lfs diff=lfs merge=lfs -text
-AMIDDA/data/train-00013-of-00016.parquet filter=lfs diff=lfs merge=lfs -text
-AMIDDA/data/train-00014-of-00016.parquet filter=lfs diff=lfs merge=lfs -text
-AMIDDA/data/train-00015-of-00016.parquet filter=lfs diff=lfs merge=lfs -text
-AMIDDA/data/validation-00000-of-00002.parquet filter=lfs diff=lfs merge=lfs -text
-AMIDDA/data/validation-00001-of-00002.parquet filter=lfs diff=lfs merge=lfs -text
-OpenITI-Makhzan_Metadata_2026-1-2/images.zip filter=lfs diff=lfs merge=lfs -text
-OpenITI-Makhzan_Metadata_2026-1-2/train.csv filter=lfs diff=lfs merge=lfs -text
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text

README.md

@@ -63,14 +63,22 @@ configs:
     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
 
-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.
+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.
@@ -95,9 +103,13 @@ it with CER/WER on held-out manuscripts. This repository is that project's data
 | **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)* |
 
-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.
+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.
@@ -121,6 +133,22 @@ AMIDDA/
   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
@@ -152,6 +180,15 @@ AMIDDA is Parquet, so the same call returns its images too, no zip handling need
 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
@@ -166,7 +203,7 @@ 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 13.3 GB, of which AMIDDA is 8.9 GB):
+**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 .
@@ -186,6 +223,7 @@ generalisation.
 | 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.
@@ -215,6 +253,10 @@ but the files are `.jpg`; this copy uses the real filenames.
 - **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.
@@ -233,6 +275,7 @@ combined use **non-commercial**.
 | 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