Go to the ppstructure
directory
cd ppstructure
download model
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
cd ..
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 \
--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 \
--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.
python3 predict_system.py --image_dir=./docs/table/1.png --table=false --ocr=false --output=../output/
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
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 \
--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 \
--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.
cd ppstructure
# download model
mkdir inference && cd inference
wget https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_ser_pretrained.tar && tar xf PP-Layout_v1.0_ser_pretrained.tar
cd ..
python3 predict_system.py --model_name_or_path=vqa/PP-Layout_v1.0_ser_pretrained/ \
--mode=vqa \
--image_dir=vqa/images/input/zh_val_0.jpg \
--vis_font_path=../doc/fonts/simfang.ttf
After the operation is completed, each image will store the visualized image in the vqa
directory under the directory specified by the output
field, and the image name is the same as the input image name.