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Mar 6, 2025
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docs: fix typos #14817

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2 changes: 1 addition & 1 deletion docs/ppocr/model_compress/knowledge_distillation.en.md
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Expand Up @@ -301,7 +301,7 @@ PostProcess:
multi_head: True # whether it is multi-head or not, if true, CTC branch is used to calculate the loss
```

Taking the above configuration as an example, the CTC decoding output of the two sub-networks `Student` and `Teahcer` will be calculated at the same time.
Taking the above configuration as an example, the CTC decoding output of the two sub-networks `Student` and `Teacher` will be calculated at the same time.
Among them, `key` is the name of the subnet, and `value` is the list of subnets.

For more specific implementation of `DistillationCTCLabelDecode`, please refer to: [rec_postprocess.py](../../ppocr/postprocess/rec_postprocess.py#L128)
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2 changes: 1 addition & 1 deletion docs/ppocr/model_compress/prune.md
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Expand Up @@ -62,7 +62,7 @@ python3.7 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_
在得到裁剪训练保存的模型后，我们可以将其导出为inference_model：

```bash linenums="1"
pytho3.7 deploy/slim/prune/export_prune_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./output/det_db/best_accuracy Global.save_inference_dir=./prune/prune_inference_model
python3.7 deploy/slim/prune/export_prune_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./output/det_db/best_accuracy Global.save_inference_dir=./prune/prune_inference_model
```

inference model的预测和部署参考：
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2 changes: 1 addition & 1 deletion docs/ppocr/model_list.en.md
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Expand Up @@ -118,4 +118,4 @@ This chapter lists OCR nb models with PP-OCRv2 or earlier versions. You can acce
|PP-OCRv2|extra-lightweight chinese OCR optimized model|11.0M|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer_opt.nb)|v2.9|
|PP-OCRv2(slim)|extra-lightweight chinese OCR optimized model|4.9M|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_opt.nb)|v2.9|
|V2.0|ppocr_v2.0 extra-lightweight chinese OCR optimized model|7.8M|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_opt.nb)|v2.9|
|V2.0(slim)|ppovr_v2.0 extra-lightweight chinese OCR optimized model|3.3M|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_slim_opt.nb)|v2.9|
|V2.0(slim)|ppocr_v2.0 extra-lightweight chinese OCR optimized model|3.3M|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_slim_opt.nb)|v2.9|
6 changes: 3 additions & 3 deletions docs/ppocr/model_train/finetune.en.md
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Expand Up @@ -4,7 +4,7 @@ comments: true

# Fine-tune

## 1. background and meaning
## 1. Background and meaning

The PP-OCR series models provided by PaddleOCR have excellent performance in general scenarios and can solve detection and recognition problems in most cases. In vertical scenarios, if you want to obtain better model, you can further improve the accuracy of the PP-OCR series detection and recognition models through fine-tune.

Expand Down Expand Up @@ -101,7 +101,7 @@ For more information on inference methods, please refer to[Paddle Inference doc]

It is recommended to choose the PP-OCRv3 model (configuration file: [ch_PP-OCRv3_rec_distillation.yml](https://github.com/PaddlePaddle/PaddleOCR/tree/main/configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml)，pre-trained model: [ch_PP-OCRv3_rec_train.tar](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_train.tar)，its accuracy and generalization performance is the best pre-training model currently available.

For more PP-OCR series models, please refer to [PP-OCR Series Model Library](../model_list.en.md)。
For more PP-OCR series models, please refer to [PP-OCR Series Model Library](../model_list.en.md).

The PP-OCRv3 model uses the GTC strategy. The SAR branch has a large number of parameters. When the training data is a simple scene, the model is easy to overfit, resulting in poor fine-tuning effect. It is recommended to remove the GTC strategy. The configuration file of the model structure is modified as follows:

Expand Down Expand Up @@ -220,7 +220,7 @@ Train:
ratio_list: [1.0, 0.1]
```

### 3.4 training optimization
### 3.4 Training optimization

The training process does not happen overnight. After completing a stage of training evaluation, it is recommended to collect and analyze the badcase of the current model in the real scene, adjust the proportion of training data in a targeted manner, or further add synthetic data. Through multiple iterations of training, the model effect is continuously optimized.

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6 changes: 3 additions & 3 deletions docs/update.en.md
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Expand Up @@ -31,7 +31,7 @@ hide:

#### 2021.8.3 released PaddleOCR v2.2, add a new structured documents analysis toolkit, i.e., [PP-Structure](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/ppstructure/README.md), support layout analysis and table recognition (One-key to export chart images to Excel files)

#### 2021.4.8 release end-to-end text recognition algorithm [PGNet](https://www.aaai.org/AAAI21Papers/AAAI-2885.WangP.pdf) which is published in AAAI 2021. Find tutorial [here](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/pgnet_en.md)；release multi language recognition [models](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/multi_languages_en.md), support more than 80 languages recognition; especically, the performance of [English recognition model](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/models_list_en.md#English) is Optimized
#### 2021.4.8 release end-to-end text recognition algorithm [PGNet](https://www.aaai.org/AAAI21Papers/AAAI-2885.WangP.pdf) which is published in AAAI 2021. Find tutorial [here](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/pgnet_en.md)；release multi language recognition [models](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/multi_languages_en.md), support more than 80 languages recognition; especially, the performance of [English recognition model](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/models_list_en.md#English) is Optimized

#### 2021.1.21 update more than 25+ multilingual recognition models [models list](./models_list_en.md), including：English, Chinese, German, French, Japanese，Spanish，Portuguese Russia Arabic and so on. Models for more languages will continue to be updated [Develop Plan](https://github.com/PaddlePaddle/PaddleOCR/issues/1048)

Expand All @@ -45,13 +45,13 @@ hide:

#### 2020.9.17 update English recognition model and Multilingual recognition model, `English`, `Chinese`, `German`, `French`, `Japanese` and `Korean` have been supported. Models for more languages will continue to be updated

#### 2020.8.24 Support the use of PaddleOCR through whl package installation，pelease refer [PaddleOCR Package](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/whl_en.md)
#### 2020.8.24 Support the use of PaddleOCR through whl package installation，please refer [PaddleOCR Package](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/whl_en.md)

#### 2020.8.16 Release text detection algorithm [SAST](https://arxiv.org/abs/1908.05498) and text recognition algorithm [SRN](https://arxiv.org/abs/2003.12294)

#### 2020.7.23, Release the playback and PPT of live class on BiliBili station, PaddleOCR Introduction, [address](https://aistudio.baidu.com/aistudio/course/introduce/1519)

#### 2020.7.15, Add mobile App demo , support both iOS and Android ( based on easyedge and Paddle Lite)
#### 2020.7.15, Add mobile App demo , support both iOS and Android (based on easyedge and Paddle Lite)

#### 2020.7.15, Improve the deployment ability, add the C + + inference , serving deployment. In addition, the benchmarks of the ultra-lightweight Chinese OCR model are provided

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