chore: import upstream snapshot with attribution

docs/version2.x/algorithm/super_resolution/algorithm_sr_gestalt.en.md

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+---
+comments: true
+---
+
+# Text Gestalt
+
+## 1. Introduction
+
+Paper:
+> [Text Gestalt: Stroke-Aware Scene Text Image Super-Resolution](https://arxiv.org/pdf/2112.08171.pdf)
+> Chen, Jingye and Yu, Haiyang and Ma, Jianqi and Li, Bin and Xue, Xiangyang
+> AAAI, 2022
+
+Referring to the [FudanOCR](https://github.com/FudanVI/FudanOCR/tree/main/text-gestalt) data download instructions, the effect of the super-score algorithm on the TextZoom test set is as follows:
+
+|Model | Backbone|config|Acc|Download link|
+|---|---|---|---|---|
+|Text Gestalt|tsrn|19.28|0.6560| [configs/sr/sr_tsrn_transformer_strock.yml](https://github.com/PaddlePaddle/PaddleOCR/tree/{{PADDLEOCR_GITHUB_REF}}/configs/sr/sr_tsrn_transformer_strock.yml)|[train model](https://paddleocr.bj.bcebos.com/sr_tsrn_transformer_strock_train.tar)|
+
+## 2. Environment
+
+Please refer to ["Environment Preparation"](../../ppocr/environment.en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](../../ppocr/blog/clone.en.md)to clone the project code.
+
+## 3. Model Training / Evaluation / Prediction
+
+Please refer to [Text Recognition Tutorial](../../ppocr/model_train/recognition.en.md). PaddleOCR modularizes the code, and training different models only requires **changing the configuration file**.
+
+### Training
+
+Specifically, after the data preparation is completed, the training can be started. The training command is as follows:
+
+```bash linenums="1"
+# Single GPU training (long training period, not recommended)
+python3 tools/train.py -c configs/sr/sr_tsrn_transformer_strock.yml
+
+# Multi GPU training, specify the gpu number through the --gpus parameter
+python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs/sr/sr_tsrn_transformer_strock.yml
+```
+
+### Evaluation
+
+```bash linenums="1"
+# GPU evaluation
+python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/sr/sr_tsrn_transformer_strock.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
+```
+
+### Prediction
+
+```bash linenums="1"
+# The configuration file used for prediction must match the training
+
+python3 tools/infer_sr.py -c configs/sr/sr_tsrn_transformer_strock.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words_en/word_52.png
+```
+
+![img](./images/word_52.png)
+
+After executing the command, the super-resolution result of the above image is as follows:
+
+![img](./images/sr_word_52.png)
+
+## 4. Inference and Deployment
+
+### 4.1 Python Inference
+
+First, the model saved during the training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/sr_tsrn_transformer_strock_train.tar) ), you can use the following command to convert:
+
+```bash linenums="1"
+python3 tools/export_model.py -c configs/sr/sr_tsrn_transformer_strock.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.save_inference_dir=./inference/sr_out
+```
+
+For Text-Gestalt super-resolution model inference, the following commands can be executed:
+
+```bash linenums="1"
+python3 tools/infer/predict_sr.py --sr_model_dir=./inference/sr_out --image_dir=doc/imgs_words_en/word_52.png --sr_image_shape=3,32,128
+```
+
+After executing the command, the super-resolution result of the above image is as follows:
+
+![img](./images/sr_word_52-20240704093810101.png)
+
+### 4.2 C++ Inference
+
+Not supported
+
+### 4.3 Serving
+
+Not supported
+
+### 4.4 More
+
+Not supported
+
+## 5. FAQ
+
+## Citation
+
+```bibtex
+@inproceedings{chen2022text,
+  title={Text gestalt: Stroke-aware scene text image super-resolution},
+  author={Chen, Jingye and Yu, Haiyang and Ma, Jianqi and Li, Bin and Xue, Xiangyang},
+  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
+  volume={36},
+  number={1},
+  pages={285--293},
+  year={2022}
+}
+```

docs/version2.x/algorithm/super_resolution/algorithm_sr_gestalt.md

@@ -0,0 +1,107 @@
+---
+typora-copy-images-to: images
+comments: true
+---
+
+# Text Gestalt
+
+## 1. 算法简介
+
+论文信息：
+> [Text Gestalt: Stroke-Aware Scene Text Image Super-Resolution](https://arxiv.org/pdf/2112.08171.pdf)
+> Chen, Jingye and Yu, Haiyang and Ma, Jianqi and Li, Bin and Xue, Xiangyang
+> AAAI, 2022
+
+参考[FudanOCR](https://github.com/FudanVI/FudanOCR/tree/main/text-gestalt) 数据下载说明，在TextZoom测试集合上超分算法效果如下：
+
+|模型|骨干网络|PSNR_Avg|SSIM_Avg|配置文件|下载链接|
+|---|---|---|---|---|---|
+|Text Gestalt|tsrn|19.28|0.6560| [configs/sr/sr_tsrn_transformer_strock.yml](https://github.com/PaddlePaddle/PaddleOCR/tree/{{PADDLEOCR_GITHUB_REF}}/configs/sr/sr_tsrn_transformer_strock.yml)|[训练模型](https://paddleocr.bj.bcebos.com/sr_tsrn_transformer_strock_train.tar)|
+
+## 2. 环境配置
+
+请先参考[《运行环境准备》](../../ppocr/environment.md)配置PaddleOCR运行环境，参考[《项目克隆》](../../ppocr/blog/clone.md)克隆项目代码。
+
+## 3. 模型训练、评估、预测
+
+请参考[文本识别训练教程](../../ppocr/model_train/recognition.md)。PaddleOCR对代码进行了模块化，训练不同的识别模型只需要**更换配置文件**即可。
+
+### 训练
+
+在完成数据准备后，便可以启动训练，训练命令如下：
+
+```bash linenums="1"
+#单卡训练（训练周期长，不建议）
+python3 tools/train.py -c configs/sr/sr_tsrn_transformer_strock.yml
+
+# 多卡训练，通过--gpus参数指定卡号
+python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs/sr/sr_tsrn_transformer_strock.yml
+```
+
+### 评估
+
+```bash linenums="1"
+# GPU 评估， Global.pretrained_model 为待测权重
+python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/sr/sr_tsrn_transformer_strock.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
+```
+
+### 预测
+
+```bash linenums="1"
+# 预测使用的配置文件必须与训练一致
+python3 tools/infer_sr.py -c configs/sr/sr_tsrn_transformer_strock.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words_en/word_52.png
+```
+
+![img](./images/word_52.png)
+
+执行命令后，上面图像的超分结果如下：
+
+![img](./images/sr_word_52.png)
+
+## 4. 推理部署
+
+### 4.1 Python推理
+
+首先将文本超分训练过程中保存的模型，转换成inference model。以 Text-Gestalt 训练的[模型](https://paddleocr.bj.bcebos.com/sr_tsrn_transformer_strock_train.tar) 为例，可以使用如下命令进行转换：
+
+```bash linenums="1"
+python3 tools/export_model.py -c configs/sr/sr_tsrn_transformer_strock.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.save_inference_dir=./inference/sr_out
+```
+
+Text-Gestalt 文本超分模型推理，可以执行如下命令：
+
+```bash linenums="1"
+python3 tools/infer/predict_sr.py --sr_model_dir=./inference/sr_out --image_dir=doc/imgs_words_en/word_52.png --sr_image_shape=3,32,128
+```
+
+执行命令后，图像的超分结果如下：
+
+![img](./images/sr_word_52-20240704093810101.png)
+
+### 4.2 C++推理
+
+暂未支持
+
+### 4.3 Serving服务化部署
+
+暂未支持
+
+### 4.4 更多推理部署
+
+暂未支持
+
+## 5. FAQ
+
+## 引用
+
+```bibtex
+@inproceedings{chen2022text,
+  title={Text gestalt: Stroke-aware scene text image super-resolution},
+  author={Chen, Jingye and Yu, Haiyang and Ma, Jianqi and Li, Bin and Xue, Xiangyang},
+  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
+  volume={36},
+  number={1},
+  pages={285--293},
+  year={2022}
+}
+```

docs/version2.x/algorithm/super_resolution/algorithm_sr_telescope.en.md

@@ -0,0 +1,110 @@
+---
+comments: true
+---
+
+# Text Gestalt
+
+## 1. Introduction
+
+Paper:
+> [Scene Text Telescope: Text-Focused Scene Image Super-Resolution](https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Scene_Text_Telescope_Text-Focused_Scene_Image_Super-Resolution_CVPR_2021_paper.pdf)
+> Chen, Jingye, Bin Li, and Xiangyang Xue
+> CVPR, 2021
+
+Referring to the [FudanOCR](https://github.com/FudanVI/FudanOCR/tree/main/scene-text-telescope) data download instructions, the effect of the super-score algorithm on the TextZoom test set is as follows:
+
+|Model|Backbone|config|Acc|Download link|
+|---|---|---|---|---|
+|Text Gestalt|tsrn|21.56|0.7411| [configs/sr/sr_telescope.yml](https://github.com/PaddlePaddle/PaddleOCR/tree/{{PADDLEOCR_GITHUB_REF}}/configs/sr/sr_telescope.yml)|[train model](https://paddleocr.bj.bcebos.com/contribution/sr_telescope_train.tar)|
+
+The [TextZoom dataset](https://paddleocr.bj.bcebos.com/dataset/TextZoom.tar) comes from two superfraction data sets, RealSR and SR-RAW, both of which contain LR-HR pairs. TextZoom has 17367 pairs of training data and 4373 pairs of test data.
+
+## 2. Environment
+
+Please refer to ["Environment Preparation"](../../ppocr/environment.en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](../../ppocr/blog/clone.en.md)to clone the project code.
+
+## 3. Model Training / Evaluation / Prediction
+
+Please refer to [Text Recognition Tutorial](../../ppocr/model_train/recognition.en.md). PaddleOCR modularizes the code, and training different models only requires **changing the configuration file**.
+
+### Training
+
+Specifically, after the data preparation is completed, the training can be started. The training command is as follows:
+
+```bash linenums="1"
+# Single GPU training (long training period, not recommended)
+python3 tools/train.py -c configs/sr/sr_telescope.yml
+
+# Multi GPU training, specify the gpu number through the --gpus parameter
+python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs/sr/sr_telescope.yml
+```
+
+### Evaluation
+
+```bash linenums="1"
+# GPU evaluation
+python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/sr/sr_telescope.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
+```
+
+### Prediction
+
+```bash linenums="1"
+# The configuration file used for prediction must match the training
+
+python3 tools/infer_sr.py -c configs/sr/sr_telescope.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words_en/word_52.png
+```
+
+![img](./images/word_52-20240704094304807.png)
+
+After executing the command, the super-resolution result of the above image is as follows:
+
+![img](./images/sr_word_52-20240704094309205.png)
+
+## 4. Inference and Deployment
+
+### 4.1 Python Inference
+
+First, the model saved during the training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/contribution/Telescope_train.tar.gz) ), you can use the following command to convert:
+
+```bash linenums="1"
+python3 tools/export_model.py -c configs/sr/sr_telescope.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.save_inference_dir=./inference/sr_out
+```
+
+For Text-Telescope super-resolution model inference, the following commands can be executed:
+
+```bash linenums="1"
+python3 tools/infer/predict_sr.py --sr_model_dir=./inference/sr_out --image_dir=doc/imgs_words_en/word_52.png --sr_image_shape=3,32,128
+
+```
+
+After executing the command, the super-resolution result of the above image is as follows:
+
+![img](./images/sr_word_52-20240704094309205.png)
+
+### 4.2 C++ Inference
+
+Not supported
+
+### 4.3 Serving
+
+Not supported
+
+### 4.4 More
+
+Not supported
+
+## 5. FAQ
+
+## Citation
+
+```bibtex
+@INPROCEEDINGS{9578891,
+  author={Chen, Jingye and Li, Bin and Xue, Xiangyang},
+  booktitle={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+  title={Scene Text Telescope: Text-Focused Scene Image Super-Resolution},
+  year={2021},
+  volume={},
+  number={},
+  pages={12021-12030},
+  doi={10.1109/CVPR46437.2021.01185}}
+```

docs/version2.x/algorithm/super_resolution/algorithm_sr_telescope.md

@@ -0,0 +1,109 @@
+---
+typora-copy-images-to: images
+comments: true
+---
+
+# Text Telescope
+
+## 1. 算法简介
+
+论文信息：
+> [Scene Text Telescope: Text-Focused Scene Image Super-Resolution](https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Scene_Text_Telescope_Text-Focused_Scene_Image_Super-Resolution_CVPR_2021_paper.pdf)
+> Chen, Jingye, Bin Li, and Xiangyang Xue
+> CVPR, 2021
+
+参考[FudanOCR](https://github.com/FudanVI/FudanOCR/tree/main/scene-text-telescope) 数据下载说明，在TextZoom测试集合上超分算法效果如下：
+
+|模型|骨干网络|PSNR_Avg|SSIM_Avg|配置文件|下载链接|
+|---|---|---|---|---|---|
+|Text Telescope|tbsrn|21.56|0.7411| [configs/sr/sr_telescope.yml](https://github.com/PaddlePaddle/PaddleOCR/tree/{{PADDLEOCR_GITHUB_REF}}/configs/sr/sr_telescope.yml)|[训练模型](https://paddleocr.bj.bcebos.com/contribution/sr_telescope_train.tar)|
+
+[TextZoom数据集](https://paddleocr.bj.bcebos.com/dataset/TextZoom.tar) 来自两个超分数据集RealSR和SR-RAW，两个数据集都包含LR-HR对，TextZoom有17367对训数据和4373对测试数据。
+
+## 2. 环境配置
+
+请先参考[《运行环境准备》](../../ppocr/environment.md)配置PaddleOCR运行环境，参考[《项目克隆》](../../ppocr/blog/clone.md)克隆项目代码。
+
+## 3. 模型训练、评估、预测
+
+请参考[文本识别训练教程](../../ppocr/model_train/recognition.md)。PaddleOCR对代码进行了模块化，训练不同的识别模型只需要**更换配置文件**即可。
+
+### 训练
+
+在完成数据准备后，便可以启动训练，训练命令如下：
+
+```bash linenums="1"
+#单卡训练（训练周期长，不建议）
+python3 tools/train.py -c configs/sr/sr_telescope.yml
+
+# 多卡训练，通过--gpus参数指定卡号
+python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs/sr/sr_telescope.yml
+```
+
+### 评估
+
+```bash linenums="1"
+# GPU 评估， Global.pretrained_model 为待测权重
+python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/sr/sr_telescope.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
+```
+
+### 预测
+
+```bash linenums="1"
+# 预测使用的配置文件必须与训练一致
+python3 tools/infer_sr.py -c configs/sr/sr_telescope.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words_en/word_52.png
+```
+
+![img](./images/word_52-20240704094304807.png)
+
+执行命令后，上面图像的超分结果如下：
+
+![img](./images/sr_word_52-20240704094309205.png)
+
+## 4. 推理部署
+
+### 4.1 Python推理
+
+首先将文本超分训练过程中保存的模型，转换成inference model。以 Text-Telescope 训练的[模型](https://paddleocr.bj.bcebos.com/contribution/Telescope_train.tar.gz) 为例，可以使用如下命令进行转换：
+
+```bash linenums="1"
+python3 tools/export_model.py -c configs/sr/sr_telescope.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.save_inference_dir=./inference/sr_out
+```
+
+Text-Telescope 文本超分模型推理，可以执行如下命令：
+
+```bash linenums="1"
+python3 tools/infer/predict_sr.py --sr_model_dir=./inference/sr_out --image_dir=doc/imgs_words_en/word_52.png --sr_image_shape=3,32,128
+```
+
+执行命令后，图像的超分结果如下：
+
+![img](./images/sr_word_52-20240704094309205.png)
+
+### 4.2 C++推理
+
+暂未支持
+
+### 4.3 Serving服务化部署
+
+暂未支持
+
+### 4.4 更多推理部署
+
+暂未支持
+
+## 5. FAQ
+
+## 引用
+
+```bibtex
+@INPROCEEDINGS{9578891,
+  author={Chen, Jingye and Li, Bin and Xue, Xiangyang},
+  booktitle={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+  title={Scene Text Telescope: Text-Focused Scene Image Super-Resolution},
+  year={2021},
+  volume={},
+  number={},
+  pages={12021-12030},
+  doi={10.1109/CVPR46437.2021.01185}}
+```