121 lines
5.8 KiB
Markdown
121 lines
5.8 KiB
Markdown
# Python Inference
|
|
|
|
- [1. Layout Structured Analysis](#1-layout-structured-analysis)
|
|
- [1.1 layout analysis + table recognition](#11-layout-analysis--table-recognition)
|
|
- [1.2 layout analysis](#12-layout-analysis)
|
|
- [1.3 table recognition](#13-table-recognition)
|
|
- [2. Key Information Extraction](#2-key-information-extraction)
|
|
- [2.1 SER](#21-ser)
|
|
- [2.2 RE+SER](#22-reser)
|
|
|
|
<a name="1"></a>
|
|
## 1. Layout Structured Analysis
|
|
Go to the `ppstructure` directory
|
|
|
|
```bash
|
|
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.
|