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docs/version3.x/module_usage/formula_recognition.en.md
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@ -334,7 +334,7 @@ You can choose either method based on your actual needs. The `predict()` method
## IV. Custom Development
If the models above do not perform well in your scenario, you can try the following steps for custom development.
Here we take training `PP-FormulaNet_plus-M` as an example. For other models, just replace the corresponding config file. First, you need to prepare a formula recognition dataset. You can follow the format of the [formula recognition demo data](https://paddle-model-ecology.bj.bcebos.com/paddlex/data/ocr_rec_latexocr_dataset_example.tar). Once the data is ready, follow the steps below to train and export the model. After export, the model can be quickly integrated into the API described above. This example uses the demo dataset. Before training the model, please ensure you have installed all PaddleOCR dependencies as described in the [installation documentation](xxx).
Here we take training `PP-FormulaNet_plus-M` as an example. For other models, just replace the corresponding config file. First, you need to prepare a formula recognition dataset. You can follow the format of the [formula recognition demo data](https://paddle-model-ecology.bj.bcebos.com/paddlex/data/ocr_rec_latexocr_dataset_example.tar). Once the data is ready, follow the steps below to train and export the model. After export, the model can be quickly integrated into the API described above. This example uses the demo dataset. Before training the model, please ensure you have installed all PaddleOCR dependencies as described in the [installation documentation](../installation.en.md).
### 4.1 Environment Setup

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docs/version3.x/module_usage/table_classification.en.md
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@ -272,8 +272,6 @@ The relevant methods, parameters, etc., are described as follows:
</tr>
</table>
For more information on how to use PaddleX's single-model inference API, you can refer to the [PaddleX Single Model Python Script Usage Instructions](../../instructions/model_python_API.md).
## 4. Secondary Development
Since PaddleOCR does not directly provide training for the table classification module, if you need to train a table classification model, you can refer to the [PaddleX Table Classification Module Secondary Development](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/table_classification.html#iv-secondary-development) section for training. The trained model can be seamlessly integrated into the PaddleOCR API for inference.