-[2. TAL Table Recognition Competition Dataset](#2-tal-table-recognition-competition-dataset)
-[2. TAL Table Recognition Competition Dataset](#2-tal-table-recognition-competition-dataset)
-[3. WTW Chinese scene table dataset](#3-wtw-chinese-scene-table-dataset)
Here are the commonly used table recognition datasets, which are being updated continuously. Welcome to contribute datasets~
Here are the commonly used table recognition datasets, which are being updated continuously. Welcome to contribute datasets~
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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) |
| PubTabNet |https://github.com/ibm-aur-nlp/PubTabNet| jsonl format, which can be loaded directly with [pubtab_dataset.py](../../../ppocr/data/pubtab_dataset.py) |
| 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) |
| 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) |
| WTW Chinese scene table dataset |https://github.com/wangwen-whu/WTW-Dataset| Conversion is required to load with [pubtab_dataset.py](../../../ppocr/data/pubtab_dataset.py)|
## 1. PubTabNet
## 1. PubTabNet
-**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.
-**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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-**Data Introduction**：The WTW Chinese scene table dataset consists of two parts: table detection and table data. The dataset contains images of two scenes, scanned and photographed.
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In this chapter, we only introduce the training of the table structure model, For model training of [text detection](../../doc/doc_en/detection_en.md) and [text recognition](../../doc/doc_en/recognition_en.md), please refer to the corresponding documents
In this chapter, we only introduce the training of the table structure model, For model training of [text detection](../../doc/doc_en/detection_en.md) and [text recognition](../../doc/doc_en/recognition_en.md), please refer to the corresponding documents
* data preparation
* data preparation
The training data uses public data set [PubTabNet](https://arxiv.org/abs/1911.10683), Can be downloaded from the official [website](https://github.com/ibm-aur-nlp/PubTabNet) 。The PubTabNet data set contains about 500,000 images, as well as annotations in html format。
For the Chinese model and the English model, the data sources are different, as follows:
English dataset: The training data uses public data set [PubTabNet](https://arxiv.org/abs/1911.10683), Can be downloaded from the official [website](https://github.com/ibm-aur-nlp/PubTabNet) 。The PubTabNet data set contains about 500,000 images, as well as annotations in html format。
Chinese dataset: The Chinese dataset consists of the following two parts, which are trained with a 1:1 sampling ratio.
> 1. Generate dataset: Use [Table Generation Tool](https://github.com/WenmuZhou/TableGeneration) to generate 40,000 images.
> 2. Crop 10,000 images from [WTW](https://github.com/wangwen-whu/WTW-Dataset).
For a detailed introduction to public datasets, please refer to [table_datasets](../../doc/doc_en/dataset/table_datasets_en.md). The following training and evaluation procedures are based on the English dataset as an example.
* Start training
* Start training
*If you are installing the cpu version of paddle, please modify the `use_gpu` field in the configuration file to false*
*If you are installing the cpu version of paddle, please modify the `use_gpu` field in the configuration file to false*
