---
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.