inference_en.md 5.8 KB
Python Inference
1. Layout Structured Analysis
Go to the ppstructure directory
cd ppstructure
````
download model
bash mkdir inference && cd inference
Download the PP-StructureV2 layout analysis model and unzip it
wget https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_layout_infer.tar && tar xf picodet_lcnet_x1_0_layout_infer.tar
Download the PP-OCRv3 text detection model and unzip it
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar && tar xf ch_PP-OCRv3_det_infer.tar
Download the PP-OCRv3 text recognition model and unzip it
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar && tar xf ch_PP-OCRv3_rec_infer.tar
Download the PP-StructureV2 form recognition model and unzip it
wget https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/ch_ppstructure_mobile_v2.0_SLANet_infer.tar && tar xf ch_ppstructure_mobile_v2.0_SLANet_infer.tar cd ..
<a name="1.1"></a>
### 1.1 layout analysis + table recognition
bash python3 predict_system.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 \
--layout_model_dir=inference/picodet_lcnet_x1_0_layout_infer \
--image_dir=./docs/table/1.png \
--rec_char_dict_path=../ppocr/utils/ppocr_keys_v1.txt \
--table_char_dict_path=../ppocr/utils/dict/table_structure_dict_ch.txt \
--output=../output \
--vis_font_path=../doc/fonts/simfang.ttf
After the operation is completed, each image will have a directory with the same name in the `structure` directory under the directory specified by the `output` field. Each table in the image will be stored as an excel, and the picture area will be cropped and saved. The filename of excel and picture is their coordinates in the image. Detailed results are stored in the `res.txt` file.
<a name="1.2"></a>
### 1.2 layout analysis
bash python3 predict_system.py --layout_model_dir=inference/picodet_lcnet_x1_0_layout_infer
--image_dir=./docs/table/1.png \
--output=../output \
--table=false \
--ocr=false
After the operation is completed, each image will have a directory with the same name in the `structure` directory under the directory specified by the `output` field. Each picture in image will be cropped and saved. The filename of picture area is their coordinates in the image. Layout analysis results will be stored in the `res.txt` file
<a name="1.3"></a>
### 1.3 table recognition
bash python3 predict_system.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 \
--image_dir=./docs/table/table.jpg \
--rec_char_dict_path=../ppocr/utils/ppocr_keys_v1.txt \
--table_char_dict_path=../ppocr/utils/dict/table_structure_dict_ch.txt \
--output=../output \
--vis_font_path=../doc/fonts/simfang.ttf \
--layout=false
After the operation is completed, each image will have a directory with the same name in the `structure` directory under the directory specified by the `output` field. Each table in the image will be stored as an excel. The filename of excel is their coordinates in the image.
<a name="2"></a>
## 2. Key Information Extraction
### 2.1 SER
bash cd ppstructure
mkdir inference && cd inference
download model
wget https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_infer.tar && tar -xf ser_vi_layoutxlm_xfund_infer.tar cd .. python3 predict_system.py \ --kie_algorithm=LayoutXLM \ --ser_model_dir=./inference/ser_vi_layoutxlm_xfund_infer \ --image_dir=./docs/kie/input/zh_val_42.jpg \ --ser_dict_path=../ppocr/utils/dict/kie_dict/xfund_class_list.txt \ --vis_font_path=../doc/fonts/simfang.ttf \ --ocr_order_method="tb-yx" \ --mode=kie
After the operation is completed, each image will store the visualized image in the `kie` directory under the directory specified by the `output` field, and the image name is the same as the input image name.
### 2.2 RE+SER
bash cd ppstructure
mkdir inference && cd inference
download model
wget https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_infer.tar && tar -xf ser_vi_layoutxlm_xfund_infer.tar wget https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_infer.tar && tar -xf re_vi_layoutxlm_xfund_infer.tar cd ..
python3 predict_system.py \ --kie_algorithm=LayoutXLM \ --re_model_dir=./inference/re_vi_layoutxlm_xfund_infer \ --ser_model_dir=./inference/ser_vi_layoutxlm_xfund_infer \ --image_dir=./docs/kie/input/zh_val_42.jpg \ --ser_dict_path=../ppocr/utils/dict/kie_dict/xfund_class_list.txt \ --vis_font_path=../doc/fonts/simfang.ttf \ --ocr_order_method="tb-yx" \ --mode=kie ```
After the operation is completed, each image will have a directory with the same name in the kie directory under the directory specified by the output field, where the visual images and prediction results are stored.