Duplicate from linxy/LaTeX_OCR

Co-authored-by: Lin Xueyuan <linxy@users.noreply.huggingface.co>

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README.md

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+---
+license: apache-2.0
+size_categories:
+- 100K<n<1M
+task_categories:
+- image-to-text
+dataset_info:
+- config_name: default
+  features:
+  - name: image
+    dtype: image
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 392473380.05
+    num_examples: 76318
+  download_size: 383401054
+  dataset_size: 392473380.05
+- config_name: full
+  features:
+  - name: image
+    dtype: image
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 385291867
+    num_examples: 76318
+  - 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
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+    num_examples: 9565
+  - name: test
+    num_bytes: 62893132.805
+    num_examples: 9593
+  download_size: 616418996
+  dataset_size: 622650817.3859999
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: full/train-*
+- 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-*
+tags:
+- code
+---
+
+# 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
+    })
+})
+```
+
+

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