cp of 8353
diff --git a/doc/doc_ch/algorithm_overview.md b/doc/doc_ch/algorithm_overview.md
index 7f6919c13a..02a4cbad95 100755
--- a/doc/doc_ch/algorithm_overview.md
+++ b/doc/doc_ch/algorithm_overview.md
@@ -3,6 +3,8 @@
 - [1. 两阶段OCR算法](#1)
   - [1.1 文本检测算法](#11)
   - [1.2 文本识别算法](#12)
+  - [1.3 文本超分辨率算法](#13)
+  - [1.4 公式识别算法](#14)
 - [2. 端到端OCR算法](#2)
 - [3. 表格识别算法](#3)
 - [4. 关键信息抽取算法](#4)
@@ -107,6 +109,34 @@ PaddleOCR将**持续新增**支持OCR领域前沿算法与模型，**欢迎广
 |RobustScanner|ResNet31| 87.77% | rec_r31_robustscanner | [训练模型](https://paddleocr.bj.bcebos.com/contribution/rec_r31_robustscanner.tar)|
 |RFL|ResNetRFL| 88.63% | rec_resnet_rfl_att | [训练模型](https://paddleocr.bj.bcebos.com/contribution/rec_resnet_rfl_att_train.tar) |
 
+
+<a name="13"></a>
+
+### 1.3 文本超分辨率算法
+已支持的文本超分辨率算法列表（戳链接获取使用教程）：
+- [x]  [Text Gestalt](./algorithm_sr_gestalt.md)
+- [x]  [Text Telescope](./algorithm_sr_telescope.md)
+
+在TextZoom公开数据集上，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](../../configs/sr/sr_tsrn_transformer_strock.yml)|[训练模型](https://paddleocr.bj.bcebos.com/sr_tsrn_transformer_strock_train.tar)|
+|Text Telescope|tbsrn|21.56|0.7411| [configs/sr/sr_telescope.yml](../../configs/sr/sr_telescope.yml)|[训练模型](https://paddleocr.bj.bcebos.com/contribution/sr_telescope_train.tar)|
+
+<a name="14"></a>
+
+### 1.4 公式识别算法
+
+已支持的公式识别算法列表（戳链接获取使用教程）：
+- [x]  [CAN](./algorithm_rec_can.md.md)
+
+在CROHME手写公式数据集上，算法效果如下：
+
+|模型    |骨干网络|配置文件|ExpRate|下载链接|
+| ----- | ----- | ----- | ----- | ----- |
+|CAN|DenseNet|[rec_d28_can.yml](../../configs/rec/rec_d28_can.yml)|51.72%|[训练模型](https://paddleocr.bj.bcebos.com/contribution/rec_d28_can_train.tar)|
+
 <a name="2"></a>
 
 ## 2. 端到端算法
diff --git a/doc/doc_ch/table_recognition.md b/doc/doc_ch/table_recognition.md
index 156ba80e37..f09dedd038 100644
--- a/doc/doc_ch/table_recognition.md
+++ b/doc/doc_ch/table_recognition.md
@@ -14,6 +14,9 @@
   - [2.5. 分布式训练](#25-分布式训练)
   - [2.6. 其他训练环境](#26-其他训练环境)
   - [2.7. 模型微调](#27-模型微调)
+    - [2.7.1 数据选择](#271-数据选择)
+    - [2.7.2 模型选择](#272-模型选择)
+    - [2.7.3 训练超参选择](#273-训练超参选择)
 - [3. 模型评估与预测](#3-模型评估与预测)
   - [3.1. 指标评估](#31-指标评估)
   - [3.2. 测试表格结构识别效果](#32-测试表格结构识别效果)
@@ -219,7 +222,39 @@ DCU设备上运行需要设置环境变量 `export HIP_VISIBLE_DEVICES=0,1,2,3`
 
 ## 2.7. 模型微调
 
-实际使用过程中，建议加载官方提供的预训练模型，在自己的数据集中进行微调，关于模型的微调方法，请参考：[模型微调教程](./finetune.md)。
+### 2.7.1 数据选择
+
+数据量：建议至少准备2000张的表格识别数据集用于模型微调。
+
+### 2.7.2 模型选择
+
+建议选择SLANet模型（配置文件：[SLANet_ch.yml](../../configs/table/SLANet_ch.yml)，预训练模型：[ch_ppstructure_mobile_v2.0_SLANet_train.tar](https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/ch_ppstructure_mobile_v2.0_SLANet_train.tar)）进行微调，其精度与泛化性能是目前提供的最优中文表格预训练模型。
+
+更多表格识别模型，请参考[PP-Structure 系列模型库](../../ppstructure/docs/models_list.md)。
+
+### 2.7.3 训练超参选择
+
+在模型微调的时候，最重要的超参就是预训练模型路径`pretrained_model`, 学习率`learning_rate`，部分配置文件如下所示。
+
+```yaml
+Global:
+  pretrained_model: ./ch_ppstructure_mobile_v2.0_SLANet_train/best_accuracy.pdparams # 预训练模型路径
+Optimizer:
+  lr:
+    name: Cosine
+    learning_rate: 0.001 #
+    warmup_epoch: 0
+  regularizer:
+    name: 'L2'
+    factor: 0
+```
+
+上述配置文件中，首先需要将`pretrained_model`字段指定为`best_accuracy.pdparams`文件路径。
+
+PaddleOCR提供的配置文件是在4卡训练（相当于总的batch size是`4*48=192`）、且没有加载预训练模型情况下的配置文件，因此您的场景中，学习率与总的batch size需要对应线性调整，例如
+
+* 如果您的场景中是单卡训练，单卡batch_size=48，则总的batch_size=48，建议将学习率调整为`0.00025`左右。
+* 如果您的场景中是单卡训练，由于显存限制，只能设置单卡batch_size=32，则总的batch_size=32，建议将学习率调整为`0.00017`左右。
 
 
 # 3. 模型评估与预测
diff --git a/doc/doc_en/algorithm_overview_en.md b/doc/doc_en/algorithm_overview_en.md
index 309d074ed4..fad0fb8a72 100755
--- a/doc/doc_en/algorithm_overview_en.md
+++ b/doc/doc_en/algorithm_overview_en.md
@@ -3,6 +3,8 @@
 - [1. Two-stage OCR Algorithms](#1)
   - [1.1 Text Detection Algorithms](#11)
   - [1.2 Text Recognition Algorithms](#12)
+  - [1.3 Text Super-Resolution Algorithms](#13)
+  - [1.4 Formula Recognition Algorithm](#14)
 - [2. End-to-end OCR Algorithms](#2)
 - [3. Table Recognition Algorithms](#3)
 - [4. Key Information Extraction Algorithms](#4)
@@ -104,6 +106,36 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r
 |RobustScanner|ResNet31| 87.77% | rec_r31_robustscanner | [trained model](https://paddleocr.bj.bcebos.com/contribution/rec_r31_robustscanner.tar)|
 |RFL|ResNetRFL| 88.63% | rec_resnet_rfl_att | [trained model](https://paddleocr.bj.bcebos.com/contribution/rec_resnet_rfl_att_train.tar) |
 
+<a name="13"></a>
+
+### 1.3 Text Super-Resolution Algorithms
+
+Supported text super-resolution algorithms (Click the link to get the tutorial):
+- [x]  [Text Gestalt](./algorithm_sr_gestalt.md)
+- [x]  [Text Telescope](./algorithm_sr_telescope.md)
+
+On the TextZoom public dataset, the effect of the algorithm is as follows:
+
+|Model|Backbone|PSNR_Avg|SSIM_Avg|Config|Download link|
+|---|---|---|---|---|---|
+|Text Gestalt|tsrn|19.28|0.6560| [configs/sr/sr_tsrn_transformer_strock.yml](../../configs/sr/sr_tsrn_transformer_strock.yml)|[trained model](https://paddleocr.bj.bcebos.com/sr_tsrn_transformer_strock_train.tar)|
+|Text Telescope|tbsrn|21.56|0.7411| [configs/sr/sr_telescope.yml](../../configs/sr/sr_telescope.yml)|[trained model](https://paddleocr.bj.bcebos.com/contribution/sr_telescope_train.tar)|
+
+<a name="14"></a>
+
+### 1.4 Formula Recognition Algorithm
+
+Supported formula recognition algorithms (Click the link to get the tutorial):
+
+- [x]  [CAN](./algorithm_rec_can.md.md)
+
+On the CROHME handwritten formula dataset, the effect of the algorithm is as follows:
+
+|Model    |Backbone|Config|ExpRate|Download link|
+| ----- | ----- | ----- | ----- | ----- |
+|CAN|DenseNet|[rec_d28_can.yml](../../configs/rec/rec_d28_can.yml)|51.72%|[trained model](https://paddleocr.bj.bcebos.com/contribution/rec_d28_can_train.tar)|
+
+
 <a name="2"></a>
 
 ## 2. End-to-end OCR Algorithms
@@ -122,7 +154,7 @@ On the PubTabNet dataset, the algorithm result is as follows:
 
 |Model|Backbone|Config|Acc|Download link|
 |---|---|---|---|---|
-|TableMaster|TableResNetExtra|[configs/table/table_master.yml](../../configs/table/table_master.yml)|77.47%|[trained](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar) / [inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar)|
+|TableMaster|TableResNetExtra|[configs/table/table_master.yml](../../configs/table/table_master.yml)|77.47%|[trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar) / [inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar)|
 
 
 <a name="4"></a>
diff --git a/doc/doc_en/table_recognition_en.md b/doc/doc_en/table_recognition_en.md
index cff2933df2..d79d98936e 100644
--- a/doc/doc_en/table_recognition_en.md
+++ b/doc/doc_en/table_recognition_en.md
@@ -14,6 +14,9 @@ This article provides a full-process guide for the PaddleOCR table recognition m
   - [2.5. Distributed Training](#25-distributed-training)
   - [2.6. Training on other platform(Windows/macOS/Linux DCU)](#26-training-on-other-platformwindowsmacoslinux-dcu)
   - [2.7. Fine-tuning](#27-fine-tuning)
+    - [2.7.1 Dataset](#271-dataset)
+    - [2.7.2 model selection](#272-model-selection)
+    - [2.7.3 Training hyperparameter selection](#273-training-hyperparameter-selection)
 - [3. Evaluation and Test](#3-evaluation-and-test)
   - [3.1. Evaluation](#31-evaluation)
   - [3.2. Test table structure recognition effect](#32-test-table-structure-recognition-effect)
@@ -226,8 +229,40 @@ Running on a DCU device requires setting the environment variable `export HIP_VI
 
 ## 2.7. Fine-tuning
 
-In the actual use process, it is recommended to load the officially provided pre-training model and fine-tune it in your own data set. For the fine-tuning method of the table recognition model, please refer to: [Model fine-tuning tutorial](./finetune.md).
 
+### 2.7.1 Dataset
+
+Data number: It is recommended to prepare at least 2000 table recognition datasets for model fine-tuning.
+
+### 2.7.2 model selection
+
+It is recommended to choose the SLANet model (configuration file: [SLANet_ch.yml](../../configs/table/SLANet_ch.yml), pre-training model: [ch_ppstructure_mobile_v2.0_SLANet_train.tar](https://paddleocr.bj.bcebos .com/ppstructure/models/slanet/ch_ppstructure_mobile_v2.0_SLANet_train.tar)) for fine-tuning, its accuracy and generalization performance is the best Chinese table pre-training model currently available.
+
+For more table recognition models, please refer to [PP-Structure Series Model Library](../../ppstructure/docs/models_list.md).
+
+### 2.7.3 Training hyperparameter selection
+
+When fine-tuning the model, the most important hyperparameters are the pretrained model path `pretrained_model`, the learning rate `learning_rate`, and some configuration files are shown below.
+
+```yaml
+Global:
+  pretrained_model: ./ch_ppstructure_mobile_v2.0_SLANet_train/best_accuracy.pdparams # Pre-trained model path
+Optimizer:
+  lr:
+    name: Cosine
+    learning_rate: 0.001 #
+    warmup_epoch: 0
+  regularizer:
+    name: 'L2'
+    factor: 0
+```
+
+In the above configuration file, you first need to specify the `pretrained_model` field as the `best_accuracy.pdparams` file path.
+
+The configuration file provided by PaddleOCR is for 4-card training (equivalent to a total batch size of `4*48=192`) and no pre-trained model is loaded. Therefore, in your scenario, the learning rate is the same as the total The batch size needs to be adjusted linearly, for example
+
+* If your scenario is single card training, single card batch_size=48, then the total batch_size=48, it is recommended to adjust the learning rate to about `0.00025`.
+* If your scenario is for single-card training, due to memory limitations, you can only set batch_size=32 for a single card, then the total batch_size=32, it is recommended to adjust the learning rate to about `0.00017`.
 
 # 3. Evaluation and Test
 
