提交 242f0adb 编写于 作者: 文幕地方's avatar 文幕地方

update inference_en.md

上级 66f4ae02
......@@ -16,23 +16,26 @@ cd ppstructure
下载模型
```bash
mkdir inference && cd inference
# 下载PP-OCRv2文本检测模型并解压
wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_quant_infer.tar && tar xf ch_PP-OCRv2_det_slim_quant_infer.tar
# 下载PP-OCRv2文本识别模型并解压
wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_infer.tar && tar xf ch_PP-OCRv2_rec_slim_quant_infer.tar
# 下载超轻量级英文表格预测模型并解压
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
# 下载PP-Structurev2版面分析模型并解压
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
# 下载PP-OCRv3文本检测模型并解压
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_slim_infer.tar && tar xf ch_PP-OCRv3_det_slim_infer.tar
# 下载PP-OCRv3文本识别模型并解压
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_slim_infer.tar && tar xf ch_PP-OCRv3_rec_slim_infer.tar
# 下载PP-Structurev2表格识别模型并解压
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 版面分析+表格识别
```bash
python3 predict_system.py --det_model_dir=inference/ch_PP-OCRv2_det_slim_quant_infer \
--rec_model_dir=inference/ch_PP-OCRv2_rec_slim_quant_infer \
--table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer \
python3 predict_system.py --det_model_dir=inference/ch_PP-OCRv3_det_slim_infer \
--rec_model_dir=inference/ch_PP-OCRv3_rec_slim_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.txt \
--table_char_dict_path=../ppocr/utils/dict/table_structure_dict_ch.txt \
--output=../output \
--vis_font_path=../doc/fonts/simfang.ttf
```
......@@ -41,19 +44,23 @@ python3 predict_system.py --det_model_dir=inference/ch_PP-OCRv2_det_slim_quant_i
<a name="1.2"></a>
### 1.2 版面分析
```bash
python3 predict_system.py --image_dir=./docs/table/1.png --table=false --ocr=false --output=../output/
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
```
运行完成后,每张图片会在`output`字段指定的目录下的`structure`目录下有一个同名目录,图片区域会被裁剪之后保存下来,图片名为表格在图片里的坐标。版面分析结果会存储在`res.txt`文件中。
<a name="1.3"></a>
### 1.3 表格识别
```bash
python3 predict_system.py --det_model_dir=inference/ch_PP-OCRv2_det_slim_quant_infer \
--rec_model_dir=inference/ch_PP-OCRv2_rec_slim_quant_infer \
--table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer \
python3 predict_system.py --det_model_dir=inference/ch_PP-OCRv3_det_slim_infer \
--rec_model_dir=inference/ch_PP-OCRv3_rec_slim_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.txt \
--table_char_dict_path=../ppocr/utils/dict/table_structure_dict_ch.txt \
--output=../output \
--vis_font_path=../doc/fonts/simfang.ttf \
--layout=false
......
......@@ -18,23 +18,26 @@ download model
```bash
mkdir inference && cd inference
# Download the PP-OCRv2 text detection model and unzip it
wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_quant_infer.tar && tar xf ch_PP-OCRv2_det_slim_quant_infer.tar
# Download the PP-OCRv2 text recognition model and unzip it
wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_infer.tar && tar xf ch_PP-OCRv2_rec_slim_quant_infer.tar
# Download the ultra-lightweight English table structure model and unzip it
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
# 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_slim_infer.tar && tar xf ch_PP-OCRv3_det_slim_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_slim_infer.tar && tar xf ch_PP-OCRv3_rec_slim_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-OCRv2_det_slim_quant_infer \
--rec_model_dir=inference/ch_PP-OCRv2_rec_slim_quant_infer \
--table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer \
python3 predict_system.py --det_model_dir=inference/ch_PP-OCRv3_det_slim_infer \
--rec_model_dir=inference/ch_PP-OCRv3_rec_slim_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.txt \
--table_char_dict_path=../ppocr/utils/dict/table_structure_dict_ch.txt \
--output=../output \
--vis_font_path=../doc/fonts/simfang.ttf
```
......@@ -43,19 +46,23 @@ After the operation is completed, each image will have a directory with the same
<a name="1.2"></a>
### 1.2 layout analysis
```bash
python3 predict_system.py --image_dir=./docs/table/1.png --table=false --ocr=false --output=../output/
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-OCRv2_det_slim_quant_infer \
--rec_model_dir=inference/ch_PP-OCRv2_rec_slim_quant_infer \
--table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer \
python3 predict_system.py --det_model_dir=inference/ch_PP-OCRv3_det_slim_infer \
--rec_model_dir=inference/ch_PP-OCRv3_rec_slim_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.txt \
--table_char_dict_path=../ppocr/utils/dict/table_structure_dict_ch.txt \
--output=../output \
--vis_font_path=../doc/fonts/simfang.ttf \
--layout=false
......
......@@ -38,7 +38,7 @@ def init_args():
parser.add_argument(
"--layout_dict_path",
type=str,
default="../ppocr/utils/dict/layout_dict/layout_pubalynet_dict.txt")
default="../ppocr/utils/dict/layout_dict/layout_publaynet_dict.txt")
parser.add_argument(
"--layout_score_threshold",
type=float,
......
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