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Return recognition position

According to the horizontal document, the recognition model not only returns the recognized content, but also the position of each word.

English document recovery

Download the inference model first

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cd PaddleOCR/ppstructure

## download model
mkdir inference && cd inference
## Download the detection model of the ultra-lightweight English PP-OCRv3 model and unzip it
https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar && tar xf en_PP-OCRv3_det_infer.tar
## Download the recognition model of the ultra-lightweight English PP-OCRv3 model and unzip it
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar && tar xf en_PP-OCRv3_rec_infer.tar
## Download the ultra-lightweight English table inch model and unzip it
wget https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/en_ppstructure_mobile_v2.0_SLANet_infer.tar
tar xf en_ppstructure_mobile_v2.0_SLANet_infer.tar
## Download the layout model of publaynet dataset and unzip it
wget https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_infer.tar
tar xf picodet_lcnet_x1_0_fgd_layout_infer.tar
cd ..

Then use the following command inference in the /ppstructure/ directory

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python predict_system.py \
--image_dir=./docs/table/1.png \
--det_model_dir=inference/en_PP-OCRv3_det_infer \
--rec_model_dir=inference/en_PP-OCRv3_rec_infer \
--rec_char_dict_path=../ppocr/utils/en_dict.txt \
--table_model_dir=inference/en_ppstructure_mobile_v2.0_SLANet_infer \
--table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt \
--layout_model_dir=inference/picodet_lcnet_x1_0_fgd_layout_infer \
--layout_dict_path=../ppocr/utils/dict/layout_dict/layout_publaynet_dict.txt \
--vis_font_path=../doc/fonts/simfang.ttf \
--recovery=True \
--output=../output/ \
--return_word_box=True

View the visualization of the inference results under ../output/structure/1/show_0.jpg, as shown below

show_0_mdf_v2

Recover Chinese documents

Download the inference model first

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cd PaddleOCR/ppstructure

## download model
cd inference
## Download the detection model of the ultra-lightweight Chinese PP-OCRv3 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 recognition model of the ultra-lightweight Chinese PP-OCRv3 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 ultra-lightweight Chinese table inch 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
## Download the layout model of CDLA dataset and unzip it
wget https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_cdla_infer.tar
tar xf picodet_lcnet_x1_0_fgd_layout_cdla_infer.tar
cd ..

Upload the following test image "2.png" to the directory ./docs/table/

2

Then use the following command inference in the /ppstructure/ directory

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python predict_system.py \
--image_dir=./docs/table/2.png \
--det_model_dir=inference/ch_PP-OCRv3_det_infer \
--rec_model_dir=inference/ch_PP-OCRv3_rec_infer \
--rec_char_dict_path=../ppocr/utils/ppocr_keys_v1.txt \
--table_model_dir=inference/ch_ppstructure_mobile_v2.0_SLANet_infer \
--table_char_dict_path=../ppocr/utils/dict/table_structure_dict_ch.txt \
--layout_model_dir=inference/picodet_lcnet_x1_0_fgd_layout_cdla_infer \
--layout_dict_path=../ppocr/utils/dict/layout_dict/layout_cdla_dict.txt \
--vis_font_path=../doc/fonts/chinese_cht.ttf \
--recovery=True \
--output=../output/ \
--return_word_box=True

View the visualization of the inference results under ../output/structure/2/show_0.jpg, as shown below

show_1_mdf_v2

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