Merge branch 'release/2.1' into add_faq

README.md

@@ -9,6 +9,7 @@ PaddleOCR supports both dynamic graph and static graph programming paradigm
 - Static graph: develop branch
 
 **Recent updates**
+- 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.
 - 2021.1.21 update more than 25+ multilingual recognition models [models list](./doc/doc_en/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).
 - 2020.12.15 update Data synthesis tool, i.e., [Style-Text](./StyleText/README.md)，easy to synthesize a large number of images which are similar to the target scene image.
 - 2020.11.25 Update a new data annotation tool, i.e., [PPOCRLabel](./PPOCRLabel/README.md), which is helpful to improve the labeling efficiency. Moreover, the labeling results can be used in training of the PP-OCR system directly.

deploy/hubserving/ocr_det/params.py

@@ -24,11 +24,6 @@ def read_params():
     cfg.use_dilation = False
     cfg.det_db_score_mode = "fast"
 
-    # #EAST parmas
-    # cfg.det_east_score_thresh = 0.8
-    # cfg.det_east_cover_thresh = 0.1
-    # cfg.det_east_nms_thresh = 0.2
-
     cfg.use_pdserving = False
     cfg.use_tensorrt = False
 

deploy/slim/prune/README.md

@@ -49,14 +49,14 @@ python3 setup.py install
 
 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.pretrain_weights="your trained model"
+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"
 ```
 
 ### 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.pretrain_weights=./output/det_db/best_accuracy  Global.save_inference_dir=inference_model
+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=inference_model
 ```
 
 inference model的预测和部署参考：

deploy/slim/prune/README_en.md

@@ -54,7 +54,7 @@ Enter the PaddleOCR root directory，perform sensitivity analysis on the model w
 
 ```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.pretrain_weights="your trained model"
+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"
 
 ```
 
@@ -63,7 +63,7 @@ python3.7 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_
 
 We can export the pruned model as inference_model for deployment:
 ```bash
-python deploy/slim/prune/export_prune_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml  -o Global.pretrain_weights=./output/det_db/best_accuracy Global.test_batch_size_per_card=1 Global.save_inference_dir=inference_model
+python 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=inference_model
 ```
 
 Reference for prediction and deployment of inference model:

deploy/slim/quantization/README.md

@@ -37,12 +37,12 @@ PaddleOCR提供了一系列训练好的[模型](../../../doc/doc_ch/models_list.
 
 量化训练的代码位于slim/quantization/quant.py 中，比如训练检测模型，训练指令如下：
 ```bash
-python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights='your trained model'   Global.save_model_dir=./output/quant_model
+python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model='your trained model'   Global.save_model_dir=./output/quant_model
 
 # 比如下载提供的训练模型
 wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
 tar -xf ch_ppocr_mobile_v2.0_det_train.tar
-python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./ch_ppocr_mobile_v2.0_det_train/best_accuracy   Global.save_inference_dir=./output/quant_inference_model
+python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./ch_ppocr_mobile_v2.0_det_train/best_accuracy   Global.save_inference_dir=./output/quant_inference_model
 
 ```
 如果要训练识别模型的量化，修改配置文件和加载的模型参数即可。

deploy/slim/quantization/README_en.md

@@ -43,13 +43,12 @@ After the quantization strategy is defined, the model can be quantified.
 
 The code for quantization training is located in `slim/quantization/quant.py`. For example, to train a detection model, the training instructions are as follows:
 ```bash
-python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights='your trained model'   Global.save_model_dir=./output/quant_model
+python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model='your trained model'   Global.save_model_dir=./output/quant_model
 
 # download provided model
 wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
 tar -xf ch_ppocr_mobile_v2.0_det_train.tar
-python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./ch_ppocr_mobile_v2.0_det_train/best_accuracy   Global.save_model_dir=./output/quant_model
-
+python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./ch_ppocr_mobile_v2.0_det_train/best_accuracy   Global.save_model_dir=./output/quant_model
 ```
 
 

doc/doc_ch/multi_languages.md

@@ -118,7 +118,7 @@ paddleocr --image_dir doc/imgs_words_en/word_308.png --det false --lang=en
 * 检测预测
 
 ```
-paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --rec false
+paddleocr --image_dir doc/imgs/11.jpg --rec false
 ```
 
 结果是一个list，每个item只包含文本框
@@ -142,7 +142,7 @@ from paddleocr import PaddleOCR, draw_ocr
 
 # 同样也是通过修改 lang 参数切换语种
 ocr = PaddleOCR(lang="korean") # 首次执行会自动下载模型文件
-img_path = 'doc/imgs/korean_1.jpg '
+img_path = 'doc/imgs/korean_1.jpg'
 result = ocr.ocr(img_path)
 # 可通过参数控制单独执行识别、检测
 # result = ocr.ocr(img_path, det=False) 只执行识别
@@ -157,7 +157,7 @@ image = Image.open(img_path).convert('RGB')
 boxes = [line[0] for line in result]
 txts = [line[1][0] for line in result]
 scores = [line[1][1] for line in result]
-im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/fonts/korean.ttf')
+im_show = draw_ocr(image, boxes, txts, scores, font_path='doc/fonts/korean.ttf')
 im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```

tools/infer/predict_det.py

@@ -65,7 +65,9 @@ class TextDetector(object):
             postprocess_params["max_candidates"] = 1000
             postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
             postprocess_params["use_dilation"] = args.use_dilation
-            postprocess_params["score_mode"] = args.det_db_score_mode
+            if hasattr(args, "det_db_score_mode"):
+                postprocess_params["score_mode"] = args.det_db_score_mode
+
         elif self.det_algorithm == "EAST":
             postprocess_params['name'] = 'EASTPostProcess'
             postprocess_params["score_thresh"] = args.det_east_score_thresh