Add LaTeX OCR dataset (90/5/5 split, 2-stage augmentation)

README.md

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----
-license: mit
----
+---
+configs:
+- config_name: default
+  data_files:
+  - split: stage1
+    path: "data/stage1-*.parquet"
+  - split: stage2
+    path: "data/stage2-*.parquet"
+  - split: validation
+    path: "data/val-*.parquet"
+  - split: test
+    path: "data/test-*.parquet"
+task_categories:
+- image-to-text
+language:
+- en
+tags:
+- latex
+- ocr
+- math
+- formula-recognition
+license: cc-by-4.0
+---
+
+# LaTeX OCR Dataset
+
+A dataset for training LaTeX OCR models that convert images of mathematical formulas into LaTeX source code.
+Built by merging and re-splitting multiple public sources, then applying two levels of augmentation for two-stage training.
+
+---
+
+## Dataset Summary
+
+| Property | Value |
+|---|---|
+| Total unique samples | ~732,952 |
+| Train (stage1) | 659,658 |
+| Train (stage2) | 659,658 |
+| Validation | 36,647 |
+| Test | 36,647 |
+| Image height | 64 px (fixed) |
+| Image width | 16 – 672 px (variable, aligned to 16px) |
+| Label format | Raw LaTeX string |
+| Max token length | 200 tokens |
+
+---
+
+## Sources
+
+All data is merged from the following public datasets, filtered, shuffled, then re-split 90/5/5:
+
+| Dataset | Config | Splits used |
+|---|---|---|
+| [linxy/LaTeX_OCR](https://huggingface.co/datasets/linxy/LaTeX_OCR) | `full` | train + validation + test |
+| [linxy/LaTeX_OCR](https://huggingface.co/datasets/linxy/LaTeX_OCR) | `synthetic_handwrite` | train + validation + test |
+| [linxy/LaTeX_OCR](https://huggingface.co/datasets/linxy/LaTeX_OCR) | `human_handwrite` | train + validation + test |
+| [OleehyO/latex-formulas](https://huggingface.co/datasets/OleehyO/latex-formulas) | `cleaned_formulas` | train |
+
+**Filtering:** samples with fewer than 2 or more than 200 LaTeX tokens are removed.
+
+---
+
+## Splits
+
+### `stage1` — Light augmentation (for Stage 1 training: encoder warm-up)
+
+Same 659,658 training samples as `stage2`, but with lighter augmentation.
+Each image is independently re-augmented, so `stage1` and `stage2` are **not identical**.
+
+Augmentations applied (each with independent probability):
+
+| Augmentation | Probability | Parameters |
+|---|---|---|
+| Gaussian blur | 0.30 | radius 0.3 – 1.2 |
+| Rotation | 0.30 | –3° to +3° |
+| Background color blend | 0.40 | random warm background |
+| Edge shadow | 0.20 | left / right / top / bottom |
+| Low resolution | 0.20 | downscale 20–60% then upscale |
+
+35% of samples are kept clean (no augmentation applied).
+
+---
+
+### `stage2` — Heavy augmentation (for Stage 2 training: LoRA fine-tuning)
+
+Same training indices as `stage1`, re-augmented with a heavier pipeline.
+
+Augmentations applied (each with independent probability):
+
+| Augmentation | Probability | Parameters |
+|---|---|---|
+| JPEG compression | 0.40 | quality 30 – 75 |
+| Low resolution | 0.40 | downscale 20–60% then upscale |
+| Gaussian noise | 0.35 | std 5 – 25 |
+| Salt & pepper noise | 0.20 | amount 1 – 5% |
+| Gaussian blur | 0.30 | radius 0.3 – 1.2 |
+| Background color blend | 0.40 | random warm background |
+| Rotation | 0.35 | –3° to +3° |
+| Perspective distortion | 0.25 | deviation 2 – 6% |
+| Random erase | 0.30 | 1–3 rectangles, white/black/gray fill |
+| Edge shadow | 0.25 | left / right / top / bottom |
+
+35% of samples are kept clean (no augmentation applied).
+
+---
+
+### `validation` and `test`
+
+Drawn from the same shuffled pool (5% each). No augmentation — images are only resized to 64px height.
+
+---
+
+## Data Format
+
+Each row contains two columns:
+
+| Column | Type | Description |
+|---|---|---|
+| `image` | `PIL.Image` (JPEG) | Formula image, height=64px, width≤672px |
+| `label` | `str` | Ground-truth LaTeX string |
+
+**Image preprocessing:**
+- Convert to RGB
+- Resize to height=64px (width scaled proportionally)
+- Width clamped to 672px maximum
+- Width aligned to nearest multiple of 16px (patch size)
+
+**LaTeX tokenization** (for reference, not stored):
+```python
+import re
+tokens = re.findall(r"\\[a-zA-Z]+|[^\s]", label)
+```
+
+---
+
+## Usage
+
+### Load the full dataset
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("harryrobert/latex-ocr")
+print(ds)
+# DatasetDict({
+#     stage1: Dataset({features: ['image', 'label'], num_rows: 659658}),
+#     stage2: Dataset({features: ['image', 'label'], num_rows: 659658}),
+#     validation: Dataset({features: ['image', 'label'], num_rows: 36647}),
+#     test: Dataset({features: ['image', 'label'], num_rows: 36647})
+# })
+```
+
+### Load a specific split
+
+```python
+train_stage1 = load_dataset("harryrobert/latex-ocr", split="stage1")
+val = load_dataset("harryrobert/latex-ocr", split="validation")
+
+sample = train_stage1[0]
+print(sample["label"])   # e.g. '\frac{1}{2}'
+sample["image"].show()   # PIL Image
+```
+
+### Streaming (large splits)
+
+```python
+ds = load_dataset("harryrobert/latex-ocr", split="stage1", streaming=True)
+for sample in ds.take(5):
+    print(sample["label"])
+```
+
+### PyTorch DataLoader integration
+
+```python
+from datasets import load_dataset
+from torch.utils.data import DataLoader
+
+ds = load_dataset("harryrobert/latex-ocr", split="stage1")
+ds = ds.with_format("torch")
+
+loader = DataLoader(ds, batch_size=32, shuffle=True)
+```
+
+---
+
+## Training Recipe
+
+This dataset is designed for a two-stage training pipeline:
+
+**Stage 1** — Train visual encoder, freeze language model decoder:
+```
+train on split="stage1"
+evaluate on split="validation"
+```
+
+**Stage 2** — LoRA fine-tuning of the full model:
+```
+train on split="stage2"
+evaluate on split="validation"
+```
+
+**Final evaluation:**
+```
+evaluate on split="test"
+```
+
+---
+
+## License
+
+Dataset contents are derived from [linxy/LaTeX_OCR](https://huggingface.co/datasets/linxy/LaTeX_OCR)
+and [OleehyO/latex-formulas](https://huggingface.co/datasets/OleehyO/latex-formulas).
+Please refer to the original datasets for their respective licenses.

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