docs: remove Python 3.7 related expressions

Author: GreatV

deploy/paddle2onnx/readme.md

@@ -97,7 +97,7 @@ After execution, the ONNX model will be saved in `./inference/det_onnx/`, `./inf
 Take the English OCR model as an example, use **ONNXRuntime** to predict and execute the following commands:
 
 ```
-python3.7 tools/infer/predict_system.py --use_gpu=False --use_onnx=True \
+python3 tools/infer/predict_system.py --use_gpu=False --use_onnx=True \
 --det_model_dir=./inference/det_onnx/model.onnx  \
 --rec_model_dir=./inference/rec_onnx/model.onnx  \
 --cls_model_dir=./inference/cls_onnx/model.onnx  \
@@ -108,7 +108,7 @@ python3.7 tools/infer/predict_system.py --use_gpu=False --use_onnx=True \
 Taking the English OCR model as an example, use **Paddle Inference** to predict and execute the following commands:
 
 ```
-python3.7 tools/infer/predict_system.py --use_gpu=False \
+python3 tools/infer/predict_system.py --use_gpu=False \
 --cls_model_dir=./inference/ch_ppocr_mobile_v2.0_cls_infer \
 --rec_model_dir=./inference/en_PP-OCRv3_rec_infer \
 --det_model_dir=./inference/en_PP-OCRv3_det_infer \

deploy/slim/prune/README.md

@@ -52,14 +52,14 @@ sen.pickle(Dict){
 
 进入PaddleOCR根目录，通过以下命令对模型进行敏感度分析训练：
 ```bash
-python3.7 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model="your trained model" Global.save_model_dir=./output/prune_model/
+python3 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model="your trained model" Global.save_model_dir=./output/prune_model/
 ```
 
 ### 4. 导出模型、预测部署
 
 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
-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
+pytho3 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：

deploy/slim/prune/README_en.md

@@ -57,7 +57,7 @@ example：
 Enter the PaddleOCR root directory，perform sensitivity analysis on the model with the following command：
 
 ```bash
-python3.7 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model="your trained model"  Global.save_model_dir=./output/prune_model/
+python3 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model="your trained model"  Global.save_model_dir=./output/prune_model/
 ```
 
 

docs/algorithm/table_recognition/algorithm_table_master.en.md

@@ -48,7 +48,7 @@ Execute the following command for model inference:
 ```bash linenums="1"
 cd ppstructure/
 # When predicting all images in a folder, you can modify image_dir to a folder, such as --image_dir='docs/table'.
-python3.7 table/predict_structure.py --table_model_dir=../output/table_master/table_structure_tablemaster_infer/ --table_algorithm=TableMaster --table_char_dict_path=../ppocr/utils/dict/table_master_structure_dict.txt --table_max_len=480 --image_dir=docs/table/table.jpg
+python3 table/predict_structure.py --table_model_dir=../output/table_master/table_structure_tablemaster_infer/ --table_algorithm=TableMaster --table_char_dict_path=../ppocr/utils/dict/table_master_structure_dict.txt --table_max_len=480 --image_dir=docs/table/table.jpg
 ```
 
 After executing the command, the prediction results of the above image (structural information and the coordinates of each cell in the table) are printed to the screen, and the visualization of the cell coordinates is also saved. An example is as follows:

docs/algorithm/table_recognition/algorithm_table_master.md

@@ -55,7 +55,7 @@ python3 tools/export_model.py -c configs/table/table_master.yml -o Global.pretra
 
 ```bash linenums="1"
 cd ppstructure/
-python3.7 table/predict_structure.py --table_model_dir=../output/table_master/table_structure_tablemaster_infer/ --table_algorithm=TableMaster --table_char_dict_path=../ppocr/utils/dict/table_master_structure_dict.txt --table_max_len=480 --image_dir=docs/table/table.jpg
+python3 table/predict_structure.py --table_model_dir=../output/table_master/table_structure_tablemaster_infer/ --table_algorithm=TableMaster --table_char_dict_path=../ppocr/utils/dict/table_master_structure_dict.txt --table_max_len=480 --image_dir=docs/table/table.jpg
 # 预测文件夹下所有图像时，可修改image_dir为文件夹，如 --image_dir='docs/table'。
 ```
 

docs/applications/轻量级车牌识别.md

@@ -316,7 +316,7 @@ python tools/eval.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_student.yml -o \
 量化训练可通过如下命令启动:
 
 ```bash linenums="1"
-python3.7 deploy/slim/quantization/quant.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_student.yml -o \
+python3 deploy/slim/quantization/quant.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_student.yml -o \
     Global.pretrained_model=output/CCPD/det/best_accuracy.pdparams \
     Global.save_model_dir=output/CCPD/det_quant \
     Global.eval_batch_step="[0, 772]" \
@@ -538,7 +538,7 @@ python tools/eval.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml -o \
 量化训练可通过如下命令启动:
 
 ```bash linenums="1"
-python3.7 deploy/slim/quantization/quant.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml -o \
+python3 deploy/slim/quantization/quant.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml -o \
     Global.pretrained_model=output/CCPD/rec/best_accuracy.pdparams \
     Global.save_model_dir=output/CCPD/rec_quant/ \
     Global.eval_batch_step="[0, 90]" \

docs/ppocr/environment.en.md

@@ -9,7 +9,7 @@ Windows and Mac users are recommended to use Anaconda to build a Python environm
 Recommended working environment:
 
 - PaddlePaddle >= 2.1.2
-- Python 3.7
+- Python 3
 - CUDA 10.1 / CUDA 10.2
 - cuDNN 7.6
 

docs/ppocr/environment.md

@@ -10,7 +10,7 @@ Windows和Mac用户推荐使用Anaconda搭建Python环境，Linux用户建议使
 推荐环境：
 
 - PaddlePaddle >= 2.1.2
-- Python 3.7
+- Python 3
 - CUDA10.1 / CUDA10.2
 - CUDNN 7.6
 

docs/ppocr/installation.en.md

@@ -8,8 +8,8 @@ After testing, PaddleOCR can run on glibc 2.23. You can also test other glibc ve
 
 PaddleOCR working environment:
 
-- PaddlePaddle 2.0.0
-- Python 3.7
+- PaddlePaddle > 2.0.0
+- Python 3
 - glibc 2.23
 
 It is recommended to use the docker provided by us to run PaddleOCR. Please refer to the docker tutorial [link](https://www.runoob.com/docker/docker-tutorial.html/).

docs/ppocr/installation.md

@@ -8,7 +8,7 @@ comments: true
 PaddleOCR 工作环境
 
 - PaddlePaddle 2.0.0
-- python3.7
+- python3
 - glibc 2.23
 - cuDNN 7.6+ (GPU)
 

docs/ppocr/model_compress/prune.en.md

@@ -58,7 +58,7 @@ The function would return a dict after loading the sensitivity file. The keys of
 Enter the PaddleOCR root directory，perform sensitivity analysis on the model with the following command：
 
 ```bash linenums="1"
-python3.7 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model="your trained model"  Global.save_model_dir=./output/prune_model/
+python3 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model="your trained model"  Global.save_model_dir=./output/prune_model/
 ```
 
 ### 5. Export inference model and deploy it

docs/ppocr/model_compress/prune.md

@@ -54,7 +54,7 @@ sen.pickle(Dict){
 进入PaddleOCR根目录，通过以下命令对模型进行敏感度分析训练：
 
 ```bash linenums="1"
-python3.7 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model="your trained model" Global.save_model_dir=./output/prune_model/
+python3 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model="your trained model" Global.save_model_dir=./output/prune_model/
 ```
 
 ### 4.导出模型、预测部署

docs/ppstructure/model_train/train_table.en.md

@@ -68,7 +68,7 @@ wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
 wget https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/paddle3.0b2/ch_ppstructure_mobile_v2.0_SLANet_infer.tar && tar xf ch_ppstructure_mobile_v2.0_SLANet_infer.tar
 cd ..
 # run
-python3.7 table/predict_table.py \
+python3 table/predict_table.py \
     --det_model_dir=inference/ch_PP-OCRv3_det_infer \
     --rec_model_dir=inference/ch_PP-OCRv3_rec_infer  \
     --table_model_dir=inference/ch_ppstructure_mobile_v2.0_SLANet_infer \

mkdocs.yml

@@ -205,6 +205,7 @@ plugins:
       branch: main
       token: !!python/object/apply:os.getenv ["MKDOCS_GIT_COMMITTERS_APIKEY"]
   - git-revision-date-localized:
+      fallback_to_build_date: false
       enable_creation_date: true
 
 markdown_extensions:

ppstructure/table/README.md

@@ -70,7 +70,7 @@ wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
 wget https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/paddle3.0b2/ch_ppstructure_mobile_v2.0_SLANet_infer.tar && tar xf ch_ppstructure_mobile_v2.0_SLANet_infer.tar
 cd ..
 # run
-python3.7 table/predict_table.py \
+python3 table/predict_table.py \
     --det_model_dir=inference/ch_PP-OCRv3_det_infer \
     --rec_model_dir=inference/ch_PP-OCRv3_rec_infer  \
     --table_model_dir=inference/ch_ppstructure_mobile_v2.0_SLANet_infer \