Add dataset card

README.md

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+---
+pretty_name: OCR Eval (Arabic synthetic documents)
+task_categories:
+- image-to-text
+language:
+- ar
+tags:
+- ocr
+- arabic
+- synthetic
+- webdataset
+- eval
+configs:
+- config_name: original
+  data_files:
+  - split: test
+    path: data/*.tar
+- config_name: augmented
+  data_files:
+  - split: test
+    path: data_aug/*.tar
+---
+
+# OCR Eval
+
+A small, fixed **evaluation** set of synthetic Arabic document images with layout
+annotations — the held-out companion to [OCR-Data/ocr_data](https://huggingface.co/datasets/OCR-Data/ocr_data).
+
+**Do not train on this.** It exists so OCR models trained on `ocr_data` can be scored on
+unseen pages from the same generator.
+
+| | |
+|---|---|
+| Originals | **1500** (lossless PNG + JSON) |
+| Augmented | **1500** — exactly one variant per original (lossless PNG + JSON) |
+| Shards | 2 (`data/shard_001.tar`, `data_aug/shard_001_aug1.tar`) |
+| Templates | the same 14 document genres as `ocr_data` |
+| Augmentations | the same 10 transforms as `ocr_data`, same weighting |
+| Language | Arabic (RTL) |
+
+## Loading
+
+```python
+from datasets import load_dataset
+
+clean = load_dataset("OCR-Data/ocr_eval", "original",  split="test")
+noisy = load_dataset("OCR-Data/ocr_eval", "augmented", split="test")
+```
+
+Both configs are WebDataset shards: files sharing a basename are one sample, so the image
+arrives as the `image` column and the annotation as `json`.
+
+## Pairing originals with their variants
+
+A variant's key is its original's key plus a two-digit slot:
+
+```
+sample_0000042      ->  data/shard_001.tar
+sample_0000042_01   ->  data_aug/shard_001_aug1.tar
+```
+
+**The shared stem is the only linkage** — there is no field inside the annotation pointing
+back at the original. Strip the `_NN` suffix to join the two configs:
+
+```python
+original_key = variant_key.rsplit("_", 1)[0]
+```
+
+## Text is unique per image
+
+This is the one place the eval set deliberately departs from how `ocr_data` was generated,
+and the reason it can be used for independent measurements.
+
+In the training set, workers share a pool of 3000 consecutive corpus pages and draw from it
+at random, so the same page renders into many images. Here, **every image is given its own
+private set of 10 corpus pages that no other image may use**, drawn from a shuffled read
+across the whole 4.6M-page source corpus rather than one contiguous window. A document
+consumes several pages (a page runs out and the next is pulled), so the guarantee is
+enforced over the whole set a page may come from, not over a single page.
+
+`meta.corpus_pages` lists the pages each document actually consumed, as truncated SHA-1
+digests of the page text, so the property is checkable rather than merely claimed:
+
+```python
+pages = [p for r in clean for p in r["json"]["meta"]["corpus_pages"]]
+assert len(pages) == len(set(pages))   # no corpus page used by two images
+```
+
+## Annotation schema
+
+```json
+{
+  "markdown": "...",
+  "dimensions": {"dpi": 96, "width": 0, "height": 0},
+  "blocks": [{"type": "title|text|table|image|header|footer",
+              "content": "...", "reading_index": 0,
+              "top_left_x": 0, "top_left_y": 0,
+              "bottom_right_x": 0, "bottom_right_y": 0}],
+  "images": [{"top_left_x": 0, "...": 0}],
+  "tables": [],
+  "header": null,
+  "footer": null,
+  "meta": {"template": "a4_report", "hybrid": null,
+           "page_font": "...", "title_font": "...",
+           "language": "ar", "script": "arabic", "direction": "rtl",
+           "corpus_pages": ["a1b2c3d4e5f6", "..."],
+           "augmentation": null}
+}
+```
+
+`meta.augmentation` is `null` throughout the `original` config and
+`{"name": ..., "params": {...}}` throughout `augmented`. Coordinates are pixels in the
+image's own space; `rotation` is the only augmentation that changes dimensions, and its
+boxes are transformed to match.
+
+Augmentation counts are the training weighting applied to 1500 draws — roughly 160 samples
+for each of `gaussian_blur`, `bad_photocopy`, `bleed_through`, `color_shift`, `letterpress`,
+`rotation`, `reflected_light`, `shadow_cast`, `gaussian_noise`, and ~37 `watermark`. Group
+by `meta.augmentation.name` to report per-transform robustness.
+
+## Caveats
+
+- **Not deduplicated against the training set.** Corpus *text* is unique per image and
+  scattered across the whole source corpus, but the generator does no content hashing, so
+  an eval layout can coincidentally resemble a training layout. This is a synthetic
+  in-distribution eval, not a contamination-proof benchmark.
+- **Not reproducible.** The generator does not seed its RNG, so this exact set of 1500
+  cannot be regenerated. These shards are the only copy of the ground truth. Each variant's
+  parameters are recorded in `meta.augmentation.params` and each page's text provenance in
+  `meta.corpus_pages`.
+- Ground truth is **synthetic**, not human-annotated: boxes and reading order come from the
+  renderer's own DOM, so they are exact by construction rather than by agreement.
+
+To get loose files back out of a shard, use `unpack_shard.py` from the generator repo.