add table dataset
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README_ch.md
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README_ch.md
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@ -132,7 +132,7 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库,助力
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- [手写中文OCR数据集](./doc/doc_ch/handwritten_datasets.md)
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- [手写中文OCR数据集](./doc/doc_ch/handwritten_datasets.md)
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- [垂类多语言OCR数据集](./doc/doc_ch/vertical_and_multilingual_datasets.md)
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- [垂类多语言OCR数据集](./doc/doc_ch/vertical_and_multilingual_datasets.md)
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- [版面分析数据集](./doc/doc_ch/layout_datasets.md)
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- [版面分析数据集](./doc/doc_ch/layout_datasets.md)
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- [表格识别数据集](./doc/doc_ch/table_datasets.md)
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- [表格识别数据集](doc/doc_ch/dataset/table_datasets.md)
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- [DocVQA数据集](./doc/doc_ch/docvqa_datasets.md)
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- [DocVQA数据集](./doc/doc_ch/docvqa_datasets.md)
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- [代码组织结构](./doc/doc_ch/tree.md)
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- [代码组织结构](./doc/doc_ch/tree.md)
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- [效果展示](#效果展示)
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- [效果展示](#效果展示)
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@ -160,13 +160,13 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库,助力
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<img src="./doc/imgs_results/ch_ppocr_mobile_v2.0/00056221.jpg" width="800">
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<img src="./doc/imgs_results/ch_ppocr_mobile_v2.0/00056221.jpg" width="800">
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<img src="./doc/imgs_results/ch_ppocr_mobile_v2.0/rotate_00052204.jpg" width="800">
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<img src="./doc/imgs_results/ch_ppocr_mobile_v2.0/rotate_00052204.jpg" width="800">
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</div>
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</div>
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</details>
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</details>
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<details open>
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<details open>
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<summary>PP-OCRv2 英文模型</summary>
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<summary>PP-OCRv2 英文模型</summary>
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<div align="center">
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<div align="center">
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<img src="./doc/imgs_results/ch_ppocr_mobile_v2.0/img_12.jpg" width="800">
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<img src="./doc/imgs_results/ch_ppocr_mobile_v2.0/img_12.jpg" width="800">
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</div>
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</div>
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<details open>
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<details open>
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<summary>PP-OCRv2 其他语言模型</summary>
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<summary>PP-OCRv2 其他语言模型</summary>
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<div align="center">
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<div align="center">
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<img src="./doc/imgs_results/french_0.jpg" width="800">
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<img src="./doc/imgs_results/french_0.jpg" width="800">
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<img src="./doc/imgs_results/korean.jpg" width="800">
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<img src="./doc/imgs_results/korean.jpg" width="800">
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</div>
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</div>
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</details>
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</details>
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<details open>
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<details open>
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@ -196,8 +196,8 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库,助力
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<div align="center">
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<div align="center">
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<img src="./ppstructure/docs/vqa/result_ser/zh_val_0_ser.jpg" width="800">
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<img src="./ppstructure/docs/vqa/result_ser/zh_val_0_ser.jpg" width="800">
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</div>
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</div>
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- RE(关系提取)
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- RE(关系提取)
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<div align="center">
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<div align="center">
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<img src="./ppstructure/docs/vqa/result_re/zh_val_21_re.jpg" width="800">
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<img src="./ppstructure/docs/vqa/result_re/zh_val_21_re.jpg" width="800">
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</div>
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</div>
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# 表格识别数据集
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- [表格识别数据集](#表格识别数据集)
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- [数据集汇总](#数据集汇总)
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- [1. PubTabNet数据集](#1-pubtabnet数据集)
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- [2. 好未来表格识别竞赛数据集](#2-好未来表格识别竞赛数据集)
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这里整理了常用版面分析数据集,持续更新中,欢迎各位小伙伴贡献数据集~
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版面分析数据集多为目标检测数据集,除了开源数据,用户还可使用合成工具自行合成,如[labelme](https://github.com/wkentaro/labelme)等。
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## 数据集汇总
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| 数据集名称 |图片下载地址| PPOCR标注下载地址 |
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| PubTabNet |https://github.com/ibm-aur-nlp/PubTabNet| jsonl格式,可直接用[pubtab_dataset.py](../../../ppocr/data/pubtab_dataset.py)加载 |
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| 好未来表格识别竞赛数据集 |https://ai.100tal.com/dataset| jsonl格式,可直接用[pubtab_dataset.py](../../../ppocr/data/pubtab_dataset.py)加载 |
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## 1. PubTabNet数据集
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- **数据简介**:PubTabNet数据集的训练集合中包含50万张图像,验证集合中包含0.9万张图像。部分图像可视化如下所示。
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<div align="center">
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<img src="../../datasets/table_PubTabNet_demo/PMC524509_007_00.png" width="500">
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<img src="../../datasets/table_PubTabNet_demo/PMC535543_007_01.png" width="500">
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</div>
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- **说明**:使用该数据集时,需要遵守[CDLA-Permissive](https://cdla.io/permissive-1-0/)协议。
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## 2. 好未来表格识别竞赛数据集
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- **数据简介**:好未来表格识别竞赛数据集的训练集合中包含1.6万张图像。验证集未给出可训练的标注。
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<div align="center">
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<img src="../../datasets/table_tal_demo/1.jpg" width="500">
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<img src="../../datasets/table_tal_demo/2.jpg" width="500">
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</div>
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# Table Recognition Datasets
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- [Table Recognition Datasets](#table-recognition-datasets)
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- [Dataset Summary](#dataset-summary)
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- [1. PubTabNet](#1-pubtabnet)
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- [2. TAL Table Recognition Competition Dataset](#2-tal-table-recognition-competition-dataset)
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Here are the commonly used layout analysis datasets, which are being updated continuously. Welcome to contribute datasets~
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## Dataset Summary
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| dataset | Image download link | PPOCR format annotation download link |
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| PubTabNet |https://github.com/ibm-aur-nlp/PubTabNet| jsonl format, which can be loaded directly with [pubtab_dataset.py](../../../ppocr/data/pubtab_dataset.py) |
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| TAL Table Recognition Competition Dataset |https://ai.100tal.com/dataset| jsonl format, which can be loaded directly with [pubtab_dataset.py](../../../ppocr/data/pubtab_dataset.py) |
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## 1. PubTabNet
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- **Data Introduction**:The training set of the PubTabNet dataset contains 500,000 images and the validation set contains 9000 images. Part of the image visualization is shown below.
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<div align="center">
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<img src="../../datasets/table_PubTabNet_demo/PMC524509_007_00.png" width="500">
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<img src="../../datasets/table_PubTabNet_demo/PMC535543_007_01.png" width="500">
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</div>
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- **illustrate**:When using this dataset, the [CDLA-Permissive](https://cdla.io/permissive-1-0/) protocol is required.
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## 2. TAL Table Recognition Competition Dataset
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- **Data Introduction**:The training set of the TAL table recognition competition dataset contains 16,000 images. The validation set does not give trainable annotations.
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<div align="center">
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<img src="../../datasets/table_tal_demo/1.jpg" width="500">
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<img src="../../datasets/table_tal_demo/2.jpg" width="500">
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</div>
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