---
license: apache-2.0
dataset_info:
- config_name: full
  features:
  - name: image
    dtype: image
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 392478490.025
    num_examples: 76319
  - name: validation
    num_bytes: 43364061.55
    num_examples: 8475
  - name: test
    num_bytes: 47643036.303
    num_examples: 9443
  download_size: 473618552
  dataset_size: 483485587.878
- config_name: human_handwrite
  features:
  - name: image
    dtype: image
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 16181778
    num_examples: 1200
  - name: validation
    num_bytes: 962283
    num_examples: 68
  - name: test
    num_bytes: 906906
    num_examples: 70
  download_size: 18056029
  dataset_size: 18050967
- config_name: human_handwrite_print
  features:
  - name: image
    dtype: image
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 3152122.8
    num_examples: 1200
  - name: validation
    num_bytes: 182615
    num_examples: 68
  - name: test
    num_bytes: 181698
    num_examples: 70
  download_size: 1336052
  dataset_size: 3516435.8
- config_name: small
  features:
  - name: image
    dtype: image
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 261296
    num_examples: 50
  - name: validation
    num_bytes: 156489
    num_examples: 30
  - name: test
    num_bytes: 156489
    num_examples: 30
  download_size: 588907
  dataset_size: 574274
- config_name: synthetic_handwrite
  features:
  - name: image
    dtype: image
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 496610333.066
    num_examples: 76266
  - name: validation
    num_bytes: 63147351.515
    num_examples: 9565
  - name: test
    num_bytes: 62893132.805
    num_examples: 9593
  download_size: 616418996
  dataset_size: 622650817.3859999
configs:
- config_name: full
  data_files:
  - split: train
    path: full/train-*
  - split: validation
    path: full/validation-*
  - split: test
    path: full/test-*
- config_name: human_handwrite
  data_files:
  - split: train
    path: human_handwrite/train-*
  - split: validation
    path: human_handwrite/validation-*
  - split: test
    path: human_handwrite/test-*
- config_name: human_handwrite_print
  data_files:
  - split: train
    path: human_handwrite_print/train-*
  - split: validation
    path: human_handwrite_print/validation-*
  - split: test
    path: human_handwrite_print/test-*
- config_name: small
  data_files:
  - split: train
    path: small/train-*
  - split: validation
    path: small/validation-*
  - split: test
    path: small/test-*
- config_name: synthetic_handwrite
  data_files:
  - split: train
    path: synthetic_handwrite/train-*
  - split: validation
    path: synthetic_handwrite/validation-*
  - split: test
    path: synthetic_handwrite/test-*
task_categories:
- image-to-text
tags:
- code
size_categories:
- 100K<n<1M
---

# LaTeX OCR 的数据仓库

本数据仓库是专为 [LaTeX_OCR](https://github.com/LinXueyuanStdio/LaTeX_OCR) 及 [LaTeX_OCR_PRO](https://github.com/LinXueyuanStdio/LaTeX_OCR) 制作的数据，来源于 `https://zenodo.org/record/56198#.V2p0KTXT6eA` 以及 `https://www.isical.ac.in/~crohme/` 以及我们自己构建。

如果这个数据仓库有帮助到你的话，请点亮 ❤️like ++

后续追加新的数据也会放在这个仓库 ~~

> 原始数据仓库在github [LinXueyuanStdio/Data-for-LaTeX_OCR](https://github.com/LinXueyuanStdio/Data-for-LaTeX_OCR).

## 数据集

本仓库有 5 个数据集

1. `small` 是小数据集，样本数 110 条，用于测试
2. `full` 是印刷体约 100k 的完整数据集。实际上样本数略小于 100k，因为用 LaTeX 的抽象语法树剔除了很多不能渲染的 LaTeX。
3. `synthetic_handwrite` 是手写体 100k 的完整数据集，基于 `full` 的公式，使用手写字体合成而来，可以视为人类在纸上的手写体。样本数实际上略小于 100k，理由同上。
4. `human_handwrite` 是手写体较小数据集，更符合人类在电子屏上的手写体。主要来源于 `CROHME`。我们用 LaTeX 的抽象语法树校验过了。
5. `human_handwrite_print` 是来自 `human_handwrite` 的印刷体数据集，公式部分和 `human_handwrite` 相同，图片部分由公式用 LaTeX 渲染而来。

## 使用

加载训练集

- name 可选 small, full, synthetic_handwrite, human_handwrite, human_handwrite_print
- split 可选 train, validation, test

```python
>>> from datasets import load_dataset
>>> train_dataset = load_dataset("linxy/LaTeX_OCR", name="small", split="train")
>>> train_dataset[2]["text"]
\rho _ { L } ( q ) = \sum _ { m = 1 } ^ { L } \ P _ { L } ( m ) \ { \frac { 1 } { q ^ { m - 1 } } } .
>>> train_dataset[2]
{'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=200x50 at 0x15A5D6CE210>,
 'text': '\\rho _ { L } ( q ) = \\sum _ { m = 1 } ^ { L } \\ P _ { L } ( m ) \\ { \\frac { 1 } { q ^ { m - 1 } } } .'}
>>> len(train_dataset)
50
```

加载所有
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("linxy/LaTeX_OCR", name="small")
>>> dataset
DatasetDict({
    train: Dataset({
        features: ['image', 'text'],
        num_rows: 50
    })
    validation: Dataset({
        features: ['image', 'text'],
        num_rows: 30
    })
    test: Dataset({
        features: ['image', 'text'],
        num_rows: 30
    })
})
```