Reasoning based on Python prediction engine

June 20, 2026 · View on GitHub

We first introduce how to convert a paddle trained model into a pytorch model, and then we will introduce text detection, text recognition, angle class, and the concatenation of them based on inference model.

CONVERT PADDLE-OCR MODEL TO PYTORCH MODEL

The PyTorch models are converted from trained models in PaddleOCR.

PaddleOCR models in BaiduPan: https://pan.baidu.com/s/1getAprT2l_JqwhjwML0g9g code：lmv7

CHINESE AND ENGLISH GENERAL OCR MODELS

python3 ./converter/ch_ppocr_mobile_v2.0_det_converter.py --src_model_path paddle_ch_ppocr_mobile_v2.0_det_train_dir

python3 ./converter/ch_ppocr_server_v2.0_det_converter.py --src_model_path paddle_ch_ppocr_server_v2.0_det_train_dir

python3 ./converter/ch_ppocr_mobile_v2.0_rec_converter.py --src_model_path paddle_ch_ppocr_mobile_v2.0_rec_train_dir

python3 ./converter/ch_ppocr_server_v2.0_rec_converter.py --src_model_path paddle_ch_ppocr_server_v2.0_rec_train_dir

python3 ./converter/ch_ppocr_mobile_v2.0_cls_converter.py --src_model_path paddle_ch_ppocr_mobile_v2.0_cls_train_dir

#ppocr v2
python3 ./converter/ch_ppocr_v2_det_converter.py --src_model_path ./paddle_ch_PP-OCRv2_det_distill_train_dir

python ./converter/ch_ppocr_v2_rec_converter.py --src_model_path ./paddle_ch_PP-OCRv2_rec_train_dir

#ppocr v3
# det
# ch_PP-OCRv3_rec_train, en_PP-OCRv3_det_distill_train, Multilingual_PP-OCRv3_det_distill_train
python ./converter/ch_ppocr_v3_det_converter.py --src_model_path paddle_ch_PP-OCRv3_det_train_dir

python ./converter/ch_ppocr_v3_rec_converter.py --src_model_path paddle_ch_PP-OCRv3_rec_train_dir

#ppocr v4

# det v4
# ch_PP-OCRv4_det
python ./converter/ch_ppocr_v4_det_converter.py --yaml_path ./configs/det/ch_PP-OCRv4/ch_PP-OCRv4_det_student.yml --src_model_path ch_PP-OCRv4_det_train_dir
# ch_PP-OCRv4_server_det
python ./converter/ch_ppocr_v4_det_server_converter.py --yaml_path ./configs/det/ch_PP-OCRv4/ch_PP-OCRv4_det_teacher.yml --src_model_path ./models_v2.7/ppocrv4/ch_PP-OCRv4_det_server_train_dir

# rec v4
# ch_PP-OCRv4_rec
python ./converter/ch_ppocr_v4_rec_converter.py --yaml_path ./configs/rec/PP-OCRv4/ch_PP-OCRv4_rec.yml --src_model_path ch_PP-OCRv4_rec_train_dir
# ch_PP-OCRv4_server_rec
python ./converter/ch_ppocr_v4_rec_server_converter.py --yaml_path ./configs/rec/PP-OCRv4/ch_PP-OCRv4_rec_hgnet.yml --src_model_path ch_PP-OCRv4_rec_server_train_dir

# PP-OCRv5
# PP-OCRv5_mobile_det
python ./converter/ppocr_v5_det_converter.py --yaml_path configs/det/PP-OCRv5/PP-OCRv5_mobile_det.yml --src_model_path PP-OCRv5_mobile_det_pretrained.pdparams
# PP-OCRv5_server_det
python ./converter/ppocr_v5_det_converter.py --yaml_path configs/det/PP-OCRv5/PP-OCRv5_server_det.yml --src_model_path PP-OCRv5_server_det_pretrained.pdparams
# PP-OCRv5_mobile_rec
python ./converter/ppocr_v5_rec_converter.py --yaml_path configs/rec/PP-OCRv5/PP-OCRv5_mobile_rec.yml --src_model_path PP-OCRv5_mobile_rec_pretrained.pdparams
# PP-OCRv5_server_rec
python ./converter/ppocr_v5_rec_converter.py --yaml_path configs/rec/PP-OCRv5/PP-OCRv5_server_rec.yml --src_model_path PP-OCRv5_server_rec_pretrained.pdparams

# PP-OCRv6
# Detection models (tiny / small / medium)
python ./converter/ppocr_v6_det_converter.py --yaml_path configs/det/PP-OCRv6/PP-OCRv6_tiny_det.yml --src_model_path PP-OCRv6_tiny_det_pretrained.pdparams
python ./converter/ppocr_v6_det_converter.py --yaml_path configs/det/PP-OCRv6/PP-OCRv6_small_det.yml --src_model_path PP-OCRv6_small_det_pretrained.pdparams
python ./converter/ppocr_v6_det_converter.py --yaml_path configs/det/PP-OCRv6/PP-OCRv6_medium_det.yml --src_model_path PP-OCRv6_medium_det_pretrained.pdparams
# Recognition models (tiny / small / medium)
python ./converter/ppocr_v6_rec_converter.py --yaml_path configs/rec/PP-OCRv6/PP-OCRv6_tiny_rec.yml --src_model_path PP-OCRv6_tiny_rec_pretrained.pdparams
python ./converter/ppocr_v6_rec_converter.py --yaml_path configs/rec/PP-OCRv6/PP-OCRv6_small_rec.yml --src_model_path PP-OCRv6_small_rec_pretrained.pdparams
python ./converter/ppocr_v6_rec_converter.py --yaml_path configs/rec/PP-OCRv6/PP-OCRv6_medium_rec.yml --src_model_path PP-OCRv6_medium_rec_pretrained.pdparams

DOCUMENT PREPROCESSING MODELS

# Document Orientation Classification (doc_ori): detect 0°/90°/180°/270° rotation
python ./converter/pplcnet_cls_converter.py --yaml_path configs/cls/doc_ori/PP-LCNet_x1_0_doc_ori.yml --src_model_path PP-LCNet_x1_0_doc_ori_pretrained.pdparams

# Text Line Orientation Classification (textline_ori): detect 0°/180° flip
# Lightweight version (0.96MB)
python ./converter/pplcnet_cls_converter.py --yaml_path configs/cls/textline_ori/PP-LCNet_x0_25_textline_ori.yml --src_model_path PP-LCNet_x0_25_textline_ori_pretrained.pdparams
# Standard version (6.5MB)
python ./converter/pplcnet_cls_converter.py --yaml_path configs/cls/textline_ori/PP-LCNet_x1_0_textline_ori.yml --src_model_path PP-LCNet_x1_0_textline_ori_pretrained.pdparams

# UVDoc Document Unwarping: correct curved/perspective-distorted documents
python ./converter/uvdoc_converter.py --src_model_path UVDoc_pretrained.pdparams

MULTILINGUAL MODELS

python3 ./converter/multilingual_mobile_v2.0_rec_converter.py --src_model_path paddle_multilingual_mobile_v2.0_rec_train_dir

# v3
# en_PP-OCRv3_rec, multilingual_PP-OCRv3_rec
python ./converter/multilingual_ppocr_v3_rec_converter.py --src_model_path paddle_multilingual_PP-OCRv3_rec_train_dir

# v4
# en_PP-OCRv4_rec
python ./converter/ch_ppocr_v4_rec_converter.py --yaml_path ./configs/rec/PP-OCRv4/en_PP-OCRv4_rec.yml --src_model_path en_PP-OCRv4_rec_train_dir

DOCUMENT STRUCTURE ANALYSIS (PP-StructureV3)

PP-StructureV3 is a document structure analysis pipeline with 8 models: layout detection, OCR, table recognition, formula recognition, seal detection, and 3 doc preprocessing models.

1. Layout Detection

python converter/ppstructure_layout_converter.py \
    --src_model_path=PP-DocLayout-M_pretrained.pdparams \
    --dst_model_path=ptocr_ppdoclayout_m.pth --variant=M

2. Table Structure Recognition

python converter/ppstructure_slanext_converter.py \
    --src_model_path=SLANeXt_wired_pretrained.pdparams \
    --dst_model_path=ptocr_slanext_wired.pth

3. Formula Recognition

python converter/ppstructure_formula_converter.py \
    --src_model_path=PP-FormulaNet_plus-M_pretrained.pdparams \
    --dst_model_path=ptocr_formulanet_m.pth --variant=M

4. Seal Detection

python converter/ppstructure_seal_converter.py \
    --src_model_path=PP-OCRv4_mobile_seal_det_pretrained.pdparams \
    --dst_model_path=ptocr_seal_det.pth

5. Doc Preprocessing

# Doc Orientation
python converter/pplcnet_cls_converter.py \
    --yaml_path=configs/cls/doc_ori/PP-LCNet_x1_0_doc_ori.yml \
    --src_model_path=PP-LCNet_x1_0_doc_ori_pretrained.pdparams \
    --output_path=ptocr_doc_ori.pth

# Text Line Orientation
python converter/pplcnet_cls_converter.py \
    --yaml_path=configs/cls/textline_ori/PP-LCNet_x1_0_textline_ori.yml \
    --src_model_path=PP-LCNet_x1_0_textline_ori_pretrained.pdparams \
    --output_path=ptocr_textline_ori.pth

# Document Unwarping
python converter/uvdoc_converter.py \
    --src_model_path=UVDoc_pretrained.pdparams \
    --output_path=ptocr_uvdoc.pth

END2END MODELS

# en_server_pgnetA
python ./converter/e2e_converter.py --yaml_path ./configs/e2e/e2e_r50_vd_pg.yml --src_model_path your_ppocr_e2e_models_en_server_pgnetA_train_dir

SUPER RESOLUTION MODELS

# sr_telescope
python ./converter/sr_converter.py --yaml_path ./configs/sr/sr_telescope.yml --src_model_path your_ppocr_sr_telescope_train_dir

OTHER DETECTION MODELS

# det_mv3_db
python3 ./converter/det_converter.py --yaml_path ./configs/det/det_mv3_db.yml --src_model_path your_ppocr_det_mv3_db_v2.0_train_dir

# det_mv3_east
python3 ./converter/det_converter.py --yaml_path ./configs/det/det_mv3_east.yml --src_model_path your_ppocr_det_mv3_east_v2.0_train_dir

# det_r50_vd_db
python3 ./converter/det_converter.py --yaml_path ./configs/det/det_r50_vd_db.yml --src_model_path your_ppocr_det_r50_vd_db_v2.0_train_dir

# det_r50_vd_east
python3 ./converter/det_converter.py --yaml_path ./configs/det/det_r50_vd_east.yml --src_model_path your_ppocr_det_r50_vd_east_v2.0_train_dir

# det_r50_vd_sast_icdar15
 python3 ./converter/det_converter.py --yaml_path ./configs/det/det_r50_vd_sast_icdar15.yml --src_model_path your_ppocr_det_r50_vd_sast_icdar15_v2.0_train_dir
 
# det_r50_vd_sast_totaltext
python3 ./converter/det_converter.py --yaml_path ./configs/det/det_r50_vd_sast_totaltext.yml --src_model_path your_ppocr_det_r50_vd_sast_totaltext_v2.0_train_dir

# det_mv3_pse
python3 ./converter/det_converter.py --yaml_path ./configs/det/det_mv3_pse.yml --src_model_path your_ppocr_det_mv3_pse_v2.0_train_dir

# det_r50_vd_pse
python3 ./converter/det_converter.py --yaml_path ./configs/det/det_r50_vd_pse.yml --src_model_path your_ppocr_det_r50_vd_pse_v2.0_train_dir

# det_fcenet
python3 ./converter/det_fcenet_converter.py --src your_det_r50_dcn_fce_ctw/det_r50_dcn_fce_ctw_v2.0_train_dir

# db++
python3 ./converter/det_converter.py --yaml_path ./configs/det/det_r50_db++_icdar15.yml --src_model_path your_ppocr_det_r50_db++_icdar15_or_td_tr_train_dir

OTHER RECOGNITION MODELS

# rec_mv3_none_none_ctc
python3 ./converter/rec_converter.py --yaml_path ./configs/rec/rec_mv3_none_none_ctc.yml --src_model_path your_ppocr_rec_mv3_none_none_ctc_v2.0_train_dir

# rec_r34_vd_none_none_ctc
python3 ./converter/rec_converter.py --yaml_path ./configs/rec/rec_r34_vd_none_none_ctc.yml --src_model_path your_ppocr_rec_r34_vd_none_none_ctc_v2.0_train_dir

# rec_mv3_none_bilstm_ctc
python3 ./converter/rec_converter.py --yaml_path ./configs/rec/rec_mv3_none_bilstm_ctc.yml --src_model_path your_ppocr_rec_mv3_none_bilstm_ctc_v2.0_train_dir

# rec_r34_vd_none_bilstm_ctc
python3 ./converter/rec_converter.py --yaml_path ./configs/rec/rec_r34_vd_none_bilstm_ctc.yml --src_model_path your_ppocr_rec_r34_vd_none_bilstm_ctc_v2.0_train_dir

# rec_mv3_tps_bilstm_ctc
python ./converter/rec_converter.py --yaml_path ./configs/rec/rec_mv3_tps_bilstm_ctc.yml --src_model_path your_ppocr_rec_mv3_tps_bilstm_ctc_v2.0_train_dir

# rec_r34_vd_tps_bilstm_ctc
python ./converter/rec_converter.py --yaml_path ./configs/rec/rec_r34_vd_tps_bilstm_ctc.yml --src_model_path your_ppocr_rec_r34_vd_tps_bilstm_ctc_v2.0_train_dir

# rec_mv3_tps_bilstm_att
python ./converter/rec_converter.py --yaml_path ./configs/rec/rec_mv3_tps_bilstm_att.yml --src_model_path your_ppocr_rec_mv3_tps_bilstm_att_v2.0_train_dir

# rec_r34_vd_tps_bilstm_att
python ./converter/rec_converter.py --yaml_path ./configs/rec/rec_r34_vd_tps_bilstm_att.yml --src_model_path your_ppocr_rec_r34_vd_tps_bilstm_att_v2.0_train_dir

# rec_r50_vd_srn
python ./converter/srn_converter.py --yaml_path ./configs/rec/rec_r50_fpn_srn.yml --src_model_path your_ppocr_rec_r50_vd_srn_train_dir

# NRTR
python ./rec_nrtr_mtb_converter.py --yaml_path ../configs/rec/rec_mtb_nrtr.yml --dict_path ../pytorchocr/utils/EN_symbol_dict.txt --src_model_path your_ppocr_rec_mtb_nrtr_train_dir

# SAR
python ./converter/rec_sar_converter.py --yaml_path ./configs/rec/rec_r31_sar.yml --dict_path ./pytorchocr/utils/dict90.txt --src_model_path your_rec_r31_sar_train_dir

# SVTR
python ./converter/rec_svtr_converter.py --yaml_path ./configs/rec/rec_svtr/rec_svtr_tiny_6local_6global_stn_en.yml --src_model_path your_rec_svtr_tiny_none_ctc_en_train_dir

# ViTSTR
python ./converter/rec_vitstr_converter.py --yaml_path ./configs/rec/rec_vitstr_none_ce.yml --src_model_path your_rec_vitstr_none_ce_train_dir

# CAN
python3 ./converter/rec_can_converter.py --yaml_path ./configs/rec/rec_d28_can.yml --src_model_path your_rec_d28_can_train_dir

INFERENCE IN PYTORCH

PyTorch models in BaiduPan: https://pan.baidu.com/s/1r1DELT8BlgxeOP2RqREJEg code：6clx

You can also get the pytorch models with the converter by yourself.

TEXT DETECTION MODEL INFERENCE

python3 ./tools/infer/predict_det.py --image_dir ./doc/imgs --model_path your_det_pth_path.pth

# v3
python ./tools/infer/predict_det.py --det_model_path your_ch_ptocr_v3_det_infer_path.pth --image_dir ./doc/imgs/1.jpg

# ppocrv4_det
python ./tools/infer/predict_det.py --image_dir ./doc/imgs/00009282.jpg --det_model_path your_ch_ptocr_v4_det_infer_path.pth --det_yaml_path ./configs/det/ch_PP-OCRv4/ch_PP-OCRv4_det_student.yml

# ppocrv4_det server
python3 ./tools/infer/predict_det.py --image_dir ./doc/imgs/00009282.jpg --det_model_path your_ch_ptocr_v4_det_server_infer_path.pth --det_yaml_path ./configs/det/ch_PP-OCRv4/ch_PP-OCRv4_det_teacher.yml

# PP-OCRv5
# PP-OCRv5_mobile_det
python ./tools/infer/predict_det.py --det_yaml_path configs/det/PP-OCRv5/PP-OCRv5_mobile_det.yml --det_model_path ./ptocr_v5_mobile_det.pth --image_dir ./doc/imgs/00009282.jpg
# PP-OCRv5_server_det
python ./tools/infer/predict_det.py --use_gpu false --det_algorithm DB --det_yaml_path configs/det/PP-OCRv5/PP-OCRv5_server_det.yml --det_model_path ./ptocr_v5_server_det.pth --image_dir ./doc/imgs/00009282.jpg

# PP-OCRv6
# PP-OCRv6_tiny_det
python ./tools/infer/predict_det.py --det_algorithm DB --det_yaml_path configs/det/PP-OCRv6/PP-OCRv6_tiny_det.yml --det_model_path ./models/v6/ptocr_v6_det_PP-OCRv6_tiny_det_pretrained.pth --image_dir ./doc/imgs/1.jpg
# PP-OCRv6_small_det
python ./tools/infer/predict_det.py --det_algorithm DB --det_yaml_path configs/det/PP-OCRv6/PP-OCRv6_small_det.yml --det_model_path ./models/v6/ptocr_v6_det_PP-OCRv6_small_det_pretrained.pth --image_dir ./doc/imgs/1.jpg
# PP-OCRv6_medium_det
python ./tools/infer/predict_det.py --det_algorithm DB --det_yaml_path configs/det/PP-OCRv6/PP-OCRv6_medium_det.yml --det_model_path ./models/v6/ptocr_v6_det_PP-OCRv6_medium_det_pretrained.pth --image_dir ./doc/imgs/1.jpg

Multilingual Detection Models

# v3
# en_ptocr_v3_det_infer.pth, multilingual_ptocr_v3_det_infer.pth
python ./tools/infer/predict_det.py --det_algorithm DB --det_yaml_path ./configs/det/det_ppocr_v3.yml --det_model_path your_multilingual_ptocr_v3_det_infer_path.pth --image_dir ./doc/imgs/1.jpg

# v4
# en_PP-OCRv4_rec
python ./tools/infer/predict_rec.py --image_dir ./doc/imgs_words/en/word_1.png --rec_model_path your_en_ptocr_v4_rec_infer_path.pth --rec_yaml_path ./configs/rec/PP-OCRv4/en_PP-OCRv4_rec.yml --rec_image_shape='3,48,320' --rec_char_dict_path ./pytorchocr/utils/en_dict.txt

TEXT RECOGNITION MODEL INFERENCE

Chinese and English General OCR Models

python3 ./tools/infer/predict_rec.py --image_dir ./doc/imgs_words --model_path your_rec_pth_path.pth

# v3
python ./tools/infer/predict_rec.py --rec_model_path your_ch_ptocr_v3_rec_infer_path.pth --rec_image_shape 3,48,320 --image_dir ./doc/imgs_words/en/word_1.png

# ppocrv4_rec
python ./tools/infer/predict_rec.py --image_dir ./doc/imgs_words/ch/word_1.jpg --rec_model_path your_ch_ptocr_v4_rec_infer_path.pth --rec_yaml_path ./configs/rec/PP-OCRv4/ch_PP-OCRv4_rec.yml --rec_image_shape='3,48,320'

# ppocrv4_rec server
python ./tools/infer/predict_rec.py --image_dir ./doc/imgs_words/ch/word_1.jpg --rec_model_path your_ch_ptocr_v4_rec_server_infer_path.pth --rec_yaml_path ./configs/rec/PP-OCRv4/ch_PP-OCRv4_rec_hgnet.yml --rec_image_shape='3,48,320'

# PP-OCRv5
# PP-OCRv5_mobile_rec
python ./tools/infer/predict_rec.py --rec_yaml_path configs/rec/PP-OCRv5/PP-OCRv5_mobile_rec.yml --rec_image_shape='3,48,320' --rec_char_dict_path ./pytorchocr/utils/dict/ppocrv5_dict.txt --rec_model_path ./ptocr_v5_mobile_rec.pth --image_dir ./doc/imgs_words/ch/word_1.jpg
# PP-OCRv5/PP-OCRv5_server_rec
python ./tools/infer/predict_rec.py --use_gpu false --rec_yaml_path configs/rec/PP-OCRv5/PP-OCRv5_server_rec.yml --rec_image_shape='3,48,320' --rec_char_dict_path ./pytorchocr/utils/dict/ppocrv5_dict.txt --rec_model_path ./ptocr_v5_server_rec.pth --image_dir ./doc/imgs_words/ch/word_1.jpg

# PP-OCRv6
# PP-OCRv6_tiny_rec (tiny dict, 49 languages)
python ./tools/infer/predict_rec.py --rec_algorithm CRNN --rec_yaml_path configs/rec/PP-OCRv6/PP-OCRv6_tiny_rec.yml --rec_image_shape='3,48,320' --rec_char_dict_path ./pytorchocr/utils/dict/ppocrv6_tiny_dict.txt --rec_model_path ./models/v6/ptocr_v6_rec_PP-OCRv6_tiny_rec_pretrained.pth --image_dir ./doc/imgs_words/ch/word_1.jpg
# PP-OCRv6_small_rec (v6 standard dict, 50 languages)
python ./tools/infer/predict_rec.py --rec_algorithm CRNN --rec_yaml_path configs/rec/PP-OCRv6/PP-OCRv6_small_rec.yml --rec_image_shape='3,48,320' --rec_char_dict_path ./pytorchocr/utils/dict/ppocrv6_dict.txt --rec_model_path ./models/v6/ptocr_v6_rec_PP-OCRv6_small_rec_pretrained.pth --image_dir ./doc/imgs_words/ch/word_1.jpg
# PP-OCRv6_medium_rec (v6 standard dict, 50 languages)
python ./tools/infer/predict_rec.py --rec_algorithm CRNN --rec_yaml_path configs/rec/PP-OCRv6/PP-OCRv6_medium_rec.yml --rec_image_shape='3,48,320' --rec_char_dict_path ./pytorchocr/utils/dict/ppocrv6_dict.txt --rec_model_path ./models/v6/ptocr_v6_rec_PP-OCRv6_medium_rec_pretrained.pth --image_dir ./doc/imgs_words/ch/word_1.jpg


Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.98458153)

Multilingual Recognition Models

If you need to predict other language models, when using inference model prediction, you need to specify the dictionary path used by --rec_char_dict_path. At the same time, in order to get the correct visualization results, You need to specify the visual font path through --vis_font_path. There are small language fonts provided by default under the doc/fonts path

# python3 ./tools/infer/predict_rec.py --image_dir ./doc/imgs_words/spanish/es_1.jpg --rec_model_dir ../rec_models/multi_language/spanish/es_mobile_v2.0_rec_infer/ --rec_char_type your_multilingual_char_type --rec_char_dict_path ./ppocr/utils/dict/your_multilingual_dict.txt

python3 ./tools/infer/predict_rec.py --rec_model_path your_japan_mobile_v2.0_rec_infer_path.pth --rec_char_type japan --rec_char_dict_path ./pytorchocr/utils/dict/japan_dict.txt --image_dir ./doc/imgs_words/japan/1.jpg

# rec_char_type
# support_character_type = [ 
#             # release/2.0
#             'ch', 'en', 'EN_symbol', 'french', 'german', 'japan', 'korean',
#             'it', 'es', 'pt', 'ru', 'ar', 'ta', 'ug', 'fa', 'ur', 'rs_latin',
#             'oc', 'rs_cyrillic', 'bg', 'uk', 'be', 'te', 'kn', 'ch_tra', 'hi',
#             'mr', 'ne', 'EN'
#             # release/2.1
#             'xi', 'pu', 'rs', 'rsc', 'ka', 'chinese_cht', 'latin', 'arabic',
#             'cyrillic', 'devanagari'
#         ]

# v3
python ./tools/infer/predict_rec.py --rec_model_path your_en_ptocr_v3_rec_infer_path.pth --rec_image_shape 3,48,320 --rec_yaml_path ./configs/rec/PP-OCRv3/en_ptocr_v3_rec.yml --rec_char_dict_path ./pytorchocr/utils/en_dict.txt  --image_dir ./doc/imgs_words/en/word_1.png 

python ./tools/infer/predict_rec.py --rec_model_path your_japan_ptocr_v3_rec_infer_path.pth --rec_image_shape 3,48,320 --rec_yaml_path ./configs/rec/PP-OCRv3/multi_language/japan_PP-OCRv3_rec.yml --rec_char_dict_path ./pytorchocr/utils/dict/japan_dict.txt  --image_dir ./doc/imgs_words/japan/1.jpg

refer to paddleocr.py

Predicts of ./doc/imgs_words/korean/1.jpg:('바탕으로', 0.9948904)

TEXT DIRECTION CLASSIFICATION MODEL IN INFERENCE

python3 ./tools/infer/predict_cls.py --image_dir ./doc/imgs_words --model_path your_cls_pth_path.pth


Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999982]

DOCUMENT PREPROCESSING MODEL INFERENCE

Document Orientation Classification (doc_ori)

Detect document rotation angle (0°/90°/180°/270°):

# doc_ori with predict_cls.py, labels: 0, 90, 180, 270
python ./tools/infer/predict_cls.py \
    --cls_yaml_path configs/cls/doc_ori/PP-LCNet_x1_0_doc_ori.yml \
    --cls_model_path pretrained/PP-LCNet_x1_0_doc_ori_infer.pth \
    --cls_image_shape 3,224,224 \
    --label_list "0" "90" "180" "270" \
    --image_dir ./doc/imgs_words/ch/word_1.jpg

Text Line Orientation Classification (textline_ori)

Detect if a text line is flipped (0°/180°):

# Lightweight version (0.96MB), labels: 0, 180
python ./tools/infer/predict_cls.py \
    --cls_yaml_path configs/cls/textline_ori/PP-LCNet_x0_25_textline_ori.yml \
    --cls_model_path pretrained/PP-LCNet_x0_25_textline_ori_infer.pth \
    --cls_image_shape 3,224,224 \
    --label_list "0" "180" \
    --image_dir ./doc/imgs_words/ch/word_1.jpg

# Standard version (6.5MB)
python ./tools/infer/predict_cls.py \
    --cls_yaml_path configs/cls/textline_ori/PP-LCNet_x1_0_textline_ori.yml \
    --cls_model_path pretrained/PP-LCNet_x1_0_textline_ori_infer.pth \
    --cls_image_shape 3,224,224 \
    --label_list "0" "180" \
    --image_dir ./doc/imgs_words/ch/word_1.jpg

UVDoc Document Unwarping

Correct curved and perspective-distorted document images. UVDoc is a standalone model, use test_new_models.py or Python API:

# Run test script
python ./tools/test_new_models.py

# Or via Python API
from pytorchocr.modeling.architectures.uvdoc_model import UVDocModel
import torch, cv2
model = UVDocModel()
model.load_state_dict(torch.load('pretrained/UVDoc_infer.pth', weights_only=False))
model.eval()
img = cv2.cvtColor(cv2.imread('warped_doc.jpg'), cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (712, 488))
x = torch.from_numpy(img.transpose(2,0,1).astype(np.float32)).unsqueeze(0)
unwarped, _ = model.unwarp(x)
cv2.imwrite('output.jpg', cv2.cvtColor(unwarped[0].permute(1,2,0).numpy().clip(0,255).astype(np.uint8), cv2.COLOR_RGB2BGR))

DOCUMENT STRUCTURE ANALYSIS INFERENCE (PP-StructureV3)

Full document structure analysis pipeline: input a document image, output structured Markdown/JSON.

Full Pipeline (all modules)

python ptstructure/predict_structure.py \
    --image_dir=./doc/imgs/ \
    --output_dir=./output/ \
    --layout_variant=M \
    --use_doc_orientation --use_doc_unwarping \
    --use_angle_cls \
    --use_formula --formula_variant=M \
    --use_seal

Basic Pipeline (layout + OCR + table)

python ptstructure/predict_structure.py \
    --image_dir=./doc/imgs/ \
    --output_dir=./output/ \
    --layout_variant=M

Layout Detection Only

python ptstructure/predict_structure.py \
    --image_dir=./doc/imgs/ --output_dir=./output/ \
    --layout_variant=M \
    --det_model_path=none --rec_model_path=none --table_model_path=none

Pipeline: Input → [doc_ori] → [UVDoc] → Layout + Global OCR → [Table/Formula/Seal] → Reading Order → Markdown/JSON

Python API

# Formula Recognition
from ptstructure.formula.ppformulanet import FormulaRecognizer
rec = FormulaRecognizer(variant='M')
rec.load_weights('models/structurev3/ptocr_formulanet_m.pth')
latex = rec.recognize(cv2.imread('formula.png'))

# Seal Detection
from ptstructure.seal.seal_det import SealDetector
det = SealDetector()
det.load_weights('models/structurev3/ptocr_seal_det.pth')
boxes, scores = det.detect(cv2.imread('seal.png'))

# Doc Preprocessing
from ptstructure.doc_preprocess import DocOrientationClassifier, UVDocUnwarper
ori = DocOrientationClassifier()
ori.load_weights('models/structurev3/ptocr_doc_ori.pth')
ori.correct_orientation(img)
uw = UVDocUnwarper()
uw.load_weights('models/structurev3/ptocr_uvdoc.pth')
uw.unwarp(img)

See PP-StructureV3 Porting Guide

TEXT DETECTION ANGLE CLASSIFICATION AND RECOGNITION INFERENCE CONCATENATION

Chinese and English OCR Models

# use direction classifier
python3 ./tools/infer/predict_system.py --image_dir ./doc/imgs --det_model_path your_det_pth_path.pth --rec_model_path your_rec_pth_path.pth --use_angle_cls --cls_model_path your_cls_pth_path.pth --vis_font_path ./doc/fonts/your_lang_font.ttf --rec_char_type your_char_type --rec_char_dict_path ./ppocr/utils/dict/your_dict.txt

# not use use direction classifier
python3 ./tools/infer/predict_system.py --image_dir ./doc/imgs --det_model_path your_det_pth_path.pth --rec_model_path your_rec_pth_path.pth --vis_font_path ./doc/fonts/your_lang_font.ttf --rec_char_type your_char_type --rec_char_dict_path ./ppocr/utils/dict/your_dict.txt

# v3
# other rec-models: use --rec_char_dict_path and --rec_yaml_path
python ./tools/infer/predict_system.py --image_dir ./doc/imgs/1.jpg --det_model_path your_ch_ptocr_v3_det_infer_path.pth --rec_image_shape 3,48,320 --rec_model_path your_ch_ptocr_v3_rec_infer_path.pth

# v4
# other det-models: use --det_yaml_path
# other rec-models: use --rec_char_dict_path, --rec_yaml_path and --rec_yaml_path
python ./tools/infer/predict_system.py --image_dir ./doc/imgs/1.jpg --det_model_path your_ch_ptocr_v4_det_infer_path.pth --det_yaml_path ./configs/det/ch_PP-OCRv4/ch_PP-OCRv4_det_student.yml --rec_image_shape 3,48,320 --rec_model_path your_ch_ptocr_v4_rec_infer_path.pth --rec_yaml_path ./configs/rec/PP-OCRv4/ch_PP-OCRv4_rec.yml

# PP-OCRv5
# mobile
python ./tools/infer/predict_system.py --use_gpu false --det_yaml_path configs/det/PP-OCRv5/PP-OCRv5_mobile_det.yml --det_model_path ./ptocr_v5_mobile_det.pth --rec_yaml_path configs/rec/PP-OCRv5/PP-OCRv5_mobile_rec.yml --rec_model_path ./ptocr_v5_mobile_rec.pth --rec_image_shape='3,48,320' --rec_char_dict_path ./pytorchocr/utils/dict/ppocrv5_dict.txt  --image_dir ./doc/imgs/1.jpg
# server
python ./tools/infer/predict_system.py --use_gpu false --det_yaml_path configs/det/PP-OCRv5/PP-OCRv5_server_det.yml --det_model_path ./ptocr_v5_server_det.pth --rec_yaml_path configs/rec/PP-OCRv5/PP-OCRv5_server_rec.yml --rec_model_path ./ptocr_v5_server_rec.pth --rec_image_shape='3,48,320' --rec_char_dict_path ./pytorchocr/utils/dict/ppocrv5_dict.txt  --image_dir ./doc/imgs/1.jpg

# PP-OCRv6 (detection + recognition pipeline)
# tiny (~1.5M params, edge/IoT, ultra-fast)
python ./tools/infer/predict_system.py --use_gpu false --det_algorithm DB --det_yaml_path configs/det/PP-OCRv6/PP-OCRv6_tiny_det.yml --det_model_path ./models/v6/ptocr_v6_det_PP-OCRv6_tiny_det_pretrained.pth --rec_algorithm CRNN --rec_yaml_path configs/rec/PP-OCRv6/PP-OCRv6_tiny_rec.yml --rec_model_path ./models/v6/ptocr_v6_rec_PP-OCRv6_tiny_rec_pretrained.pth --rec_image_shape='3,48,320' --rec_char_dict_path ./pytorchocr/utils/dict/ppocrv6_tiny_dict.txt --image_dir ./doc/imgs/1.jpg
# small (~7.8M params, mobile/desktop, balanced speed & accuracy)
python ./tools/infer/predict_system.py --use_gpu false --det_algorithm DB --det_yaml_path configs/det/PP-OCRv6/PP-OCRv6_small_det.yml --det_model_path ./models/v6/ptocr_v6_det_PP-OCRv6_small_det_pretrained.pth --rec_algorithm CRNN --rec_yaml_path configs/rec/PP-OCRv6/PP-OCRv6_small_rec.yml --rec_model_path ./models/v6/ptocr_v6_rec_PP-OCRv6_small_rec_pretrained.pth --rec_image_shape='3,48,320' --rec_char_dict_path ./pytorchocr/utils/dict/ppocrv6_dict.txt --image_dir ./doc/imgs/1.jpg
# medium (~35M params, server, highest accuracy)
python ./tools/infer/predict_system.py --use_gpu false --det_algorithm DB --det_yaml_path configs/det/PP-OCRv6/PP-OCRv6_medium_det.yml --det_model_path ./models/v6/ptocr_v6_det_PP-OCRv6_medium_det_pretrained.pth --rec_algorithm CRNN --rec_yaml_path configs/rec/PP-OCRv6/PP-OCRv6_medium_rec.yml --rec_model_path ./models/v6/ptocr_v6_rec_PP-OCRv6_medium_rec_pretrained.pth --rec_image_shape='3,48,320' --rec_char_dict_path ./pytorchocr/utils/dict/ppocrv6_dict.txt --image_dir ./doc/imgs/1.jpg

After executing the command, the recognition result image is as follows:

END2END MODEL INFERENCE

# en_server_pgnetA
python tools/infer/predict_e2e.py --e2e_model_path ./en_server_pgnetA_infer.pth --image_dir ./doc/imgs_en/img623.jpg --e2e_algorithm PGNet --e2e_pgnet_polygon True --e2e_char_dict_path ./pytorchocr/utils/ic15_dict.txt --e2e_yaml_path ./configs/e2e/e2e_r50_vd_pg.yml

SUPER RESOLUTION MODEL INFERENCE

# sr_telescope
python ./tools/infer/predict_sr.py --sr_yaml_path ./configs/sr/sr_telescope.yml --sr_model_path your_sr_telescope_infer_path.pth --image_dir
./doc/imgs_words_en/word_52.png

OTHER MODEL INFERENCE

If you want to try other detection algorithms or recognition algorithms, please refer to the above text detection model inference and text recognition model inference, update the corresponding configuration and model.

# detection
# det_mv3_db
python3 ./tools/infer/predict_det.py --det_model_path your_det_mv3_db_v2.0_infer_path.pth --image_dir ./doc/imgs_en/img_195.jpg  --det_algorithm DB --det_yaml_path ./configs/det/det_mv3_db.yml

# det_mv3_east
python3 ./tools/infer/predict_det.py --det_model_path your_det_mv3_east_v2.0_infer_path.pth --image_dir ./doc/imgs_en/img_195.jpg  --det_algorithm EAST --det_yaml_path ./configs/det/det_mv3_east.yml

# det_r50_vd_db
python3 ./tools/infer/predict_det.py --det_model_path your_det_r50_vd_db_v2.0_infer_path.pth --image_dir ./doc/imgs_en/img_195.jpg  --det_algorithm DB --det_yaml_path ./configs/det/det_r50_vd_db.yml

# det_r50_vd_east
python3 ./tools/infer/predict_det.py --det_model_path your_det_r50_vd_east_v2.0_infer_path.pth --image_dir ./doc/imgs_en/img_195.jpg  --det_algorithm EAST --det_yaml_path ./configs/det/det_r50_vd_east.yml

# det_r50_vd_sast_icdar15
python ./tools/infer/predict_det.py --det_model_path your_det_r50_vd_sast_icdar15_v2.0_infer_path.pth --image_dir ./doc/imgs/00006737.jpg  --det_algorithm SAST --det_yaml_path ./configs/det/det_r50_vd_sast_icdar15.yml

# det_r50_vd_sast_totaltext
python3 ./tools/infer/predict_det.py --det_model_path your_det_r50_vd_sast_totaltext_v2.0_infer_path.pth --image_dir ./doc/imgs/00006737.jpg  --det_algorithm SAST --det_yaml_path ./configs/det/det_r50_vd_sast_totaltext.yml

# det_mv3_pse
python3 ./tools/infer/predict_det.py --det_model_path your_det_mv3_pse_v2.0_infer_path.pth --image_dir ./doc/imgs_en/img_195.jpg  --det_algorithm PSE --det_yaml_path ./configs/det/det_mv3_pse.yml

# det_r50_vd_pse
python3 ./tools/infer/predict_det.py --det_model_path your_det_r50_vd_pse_v2.0_infer_path.pth --image_dir ./doc/imgs_en/img_195.jpg  --det_algorithm PSE --det_yaml_path ./configs/det/det_r50_vd_pse.yml

# ppocr_v3_det
python ./tools/infer/predict_det.py --use_gpu false --det_algorithm DB --det_model_path your_xx_ptocr_v3_det_infer_path.pth --image_dir ./doc/imgs/1.jpg

# det_fcenet
python3.7 ./tools/infer/predict_det.py --det_algorithm FCE --det_model_path your_det_r50_dcn_fce_ctw_v2.0_infer_path.pth --det_fce_box_type poly --det_yaml_path ./configs/det/det_r50_vd_dcn_fce_ctw.yml --image_dir ./doc/imgs_en/img_10.jpg

# db++
python3 tools/infer/predict_det.py --image_dir ./doc/imgs_en/img_10.jpg --det_model_path your_det_r50_db++_icdar15_infer_path.pth --det_algorithm="DB++"  --det_yaml_path ./configs/det/det_r50_db++_icdar15.yml
python3 tools/infer/predict_det.py --image_dir ./doc/imgs_en/img_10.jpg --det_model_path your_det_r50_db++_td_tr_infer_path.pth --det_algorithm="DB++"  --det_yaml_path ./configs/det/det_r50_db++_td_tr.yml


# recognition
# rec_mv3_none_none_ctc
python3 ./tools/infer/predict_rec.py --rec_model_path your_rec_mv3_none_none_ctc_v2.0_infer_path.pth --image_dir ./doc/imgs_words_en/word_10.png --rec_char_dict_path ./pytorchocr/utils/dict/en_dict.txt --rec_char_type en --rec_yaml_path ./configs/rec/rec_mv3_none_none_ctc.yml

# rec_r34_vd_none_none_ctc
 python3 ./tools/infer/predict_rec.py --rec_model_path your_rec_r34_vd_none_none_ctc_v2.0_infer_path.pth --image_dir ./doc/imgs_words_en/word_201.png --rec_char_dict_path ./pytorchocr/utils/dict/en_dict.txt --rec_char_type en --rec_yaml_path ./configs/rec/rec_r34_vd_none_none_ctc.yml

# rec_mv3_none_bilstm_ctc
python3 ./tools/infer/predict_rec.py --rec_model_path your_rec_mv3_none_bilstm_ctc_v2.0_infer_path.pth --image_dir ./doc/imgs_words_en/word_10.png --rec_char_dict_path ./pytorchocr/utils/dict/en_dict.txt --rec_char_type en --rec_yaml_path ./configs/rec/rec_mv3_none_bilstm_ctc.yml

# rec_r34_vd_none_bilstm_ctc
python3 ./tools/infer/predict_rec.py --rec_model_path your_rec_r34_vd_none_bilstm_ctc_v2.0_infer_path.pth --image_dir ./doc/imgs_words_en/word_10.png --rec_char_dict_path ./pytorchocr/utils/dict/en_dict.txt --rec_char_type en --rec_yaml_path ./configs/rec/rec_r34_vd_none_bilstm_ctc.yml
 
# rec_mv3_tps_bilstm_ctc
python ./tools/infer/predict_rec.py --rec_model_path your_rec_mv3_tps_bilstm_ctc_v2.0_infer_path.pth --image_dir ./doc/imgs_words_en/word_401.png --rec_image_shape 3,32,100 --rec_char_type en --rec_yaml_path ./configs/rec/rec_mv3_tps_bilstm_ctc.yml
 
# rec_r34_vd_tps_bilstm_ctc
python ./tools/infer/predict_rec.py --rec_model_path your_rec_r34_vd_tps_bilstm_ctc_v2.0_infer_path.pth --image_dir ./doc/imgs_words_en/word_401.png --rec_image_shape 3,32,100 --rec_char_type en --rec_yaml_path ./configs/rec/rec_r34_vd_tps_bilstm_ctc.yml

# rec_mv3_tps_bilstm_att
python ./tools/infer/predict_rec.py --rec_model_path your_rec_mv3_tps_bilstm_att_v2.0_infer_path.pth --image_dir ./doc/imgs_words_en/word_461.png --rec_image_shape 3,32,100 --rec_char_type en  --rec_algorithm RARE --rec_yaml_path ./configs/rec/rec_mv3_tps_bilstm_att.yml

# rec_r34_vd_tps_bilstm_att
python ./tools/infer/predict_rec.py --rec_model_path your_rec_r34_vd_tps_bilstm_att_v2.0_infer_path.pth --image_dir ./doc/imgs_words_en/word_461.png  --rec_image_shape 3,32,100 --rec_char_type en  --rec_algorithm RARE --rec_yaml_path ./configs/rec/rec_r34_vd_tps_bilstm_att.yml

# rec_r50_vd_srn
python ./tools/infer/predict_rec.py --rec_model_path your_rec_r50_vd_srn_infer_path.pth --image_dir ./doc/imgs_words_en/word_401.png --rec_image_shape 1,64,256 --rec_char_type en --rec_algorithm SRN --rec_yaml_path ./configs/rec/rec_r50_fpn_srn.yml

# NRTR
python ./tools/infer/predict_rec.py --rec_char_dict_path ./pytorchocr/utils/EN_symbol_dict.txt --rec_algorithm NRTR --rec_model_path your_rec_mtb_nrtr_infer_path.pth --rec_yaml_path ./configs/rec/rec_mtb_nrtr.yml --image_dir ./doc/imgs_words_en/word_10.png --rec_image_shape 1,32,100

# SAR
python ./tools/infer/predict_rec.py --rec_char_dict_path ./pytorchocr/utils/dict90.txt --max_text_length 30 --rec_yaml_path ./configs/rec/rec_r31_sar.yml --rec_algorithm SAR --rec_image_shape 3,48,48,160 --use_space_char false --rec_model_path your_rec_r31_sar_infer_path.pth --image_dir ./doc/imgs_words/en/word_1.png

# SVTR
# en
python ./tools/infer/predict_rec.py --rec_model_path yout_rec_svtr_tiny_none_ctc_en_infer_path.pth --rec_algorithm SVTR --rec_image_shape 3,64,256 --rec_char_dict_path ./pytorchocr/utils/ic15_dict.txt --rec_yaml_path ./configs/rec/rec_svtr/rec_svtr_tiny_6local_6global_stn_en.yml --image_dir ./doc/imgs_words_en/word_10.png
# ch
python ./tools/infer/predict_rec.py --rec_model_path yout_rec_svtr_tiny_none_ctc_ch_infer_path.pth --rec_algorithm SVTR --rec_image_shape 3,64,256 --rec_char_dict_path ./pytorchocr/utils/ppocr_keys_v1.txt --rec_yaml_path ./configs/rec/rec_svtr/rec_svtr_tiny_6local_6global_stn_ch.yml --image_dir ./doc/imgs_words/ch/word_1.jpg

# ViTSTR
python tools/infer/predict_rec.py --rec_model_path your_rec_vitstr_none_ce_infer_path.pth --rec_algorithm ViTSTR --rec_image_shape 1,224,224  --rec_char_dict_path ./pytorchocr/utils/EN_symbol_dict.txt --rec_yaml_path ./configs/rec/rec_vitstr_none_ce.yml --image_dir ./doc/imgs_words_en/word_10.png

# CAN
python3 ./tools/infer/predict_rec.py --image_dir="./doc/datasets/crohme_demo/hme_00.jpg" --rec_algorithm="CAN" --rec_batch_num=1 --rec_model_path your_rec_d28_can_infer_path.pth --rec_char_dict_path="./pytorchocr/utils/dict/latex_symbol_dict.txt" --rec_yaml_path ./configs/rec/rec_d28_can.yml

PARSER LIST

def init_args():
    def str2bool(v):
        return v.lower() in ("true", "t", "1")

    parser = argparse.ArgumentParser()
    # params for prediction engine
    parser.add_argument("--use_gpu", type=str2bool, default=True)
    # parser.add_argument("--ir_optim", type=str2bool, default=True)
    # parser.add_argument("--use_tensorrt", type=str2bool, default=False)
    # parser.add_argument("--use_fp16", type=str2bool, default=False)
    parser.add_argument("--gpu_mem", type=int, default=500)
    parser.add_argument("--warmup", type=str2bool, default=False)

    # params for text detector
    parser.add_argument("--image_dir", type=str)
    parser.add_argument("--det_algorithm", type=str, default='DB')
    parser.add_argument("--det_model_path", type=str)
    parser.add_argument("--det_limit_side_len", type=float, default=960)
    parser.add_argument("--det_limit_type", type=str, default='max')
    parser.add_argument("--det_box_type", type=str, default="quad")

    # DB parmas
    parser.add_argument("--det_db_thresh", type=float, default=0.3)
    parser.add_argument("--det_db_box_thresh", type=float, default=0.6)
    parser.add_argument("--det_db_unclip_ratio", type=float, default=1.5)
    parser.add_argument("--max_batch_size", type=int, default=10)
    parser.add_argument("--use_dilation", type=str2bool, default=False)
    parser.add_argument("--det_db_score_mode", type=str, default="fast")

    # EAST parmas
    parser.add_argument("--det_east_score_thresh", type=float, default=0.8)
    parser.add_argument("--det_east_cover_thresh", type=float, default=0.1)
    parser.add_argument("--det_east_nms_thresh", type=float, default=0.2)

    # SAST parmas
    parser.add_argument("--det_sast_score_thresh", type=float, default=0.5)
    parser.add_argument("--det_sast_nms_thresh", type=float, default=0.2)
    parser.add_argument("--det_sast_polygon", type=str2bool, default=False)

    # PSE parmas
    parser.add_argument("--det_pse_thresh", type=float, default=0)
    parser.add_argument("--det_pse_box_thresh", type=float, default=0.85)
    parser.add_argument("--det_pse_min_area", type=float, default=16)
    parser.add_argument("--det_pse_box_type", type=str, default='box')
    parser.add_argument("--det_pse_scale", type=int, default=1)

    # FCE parmas
    parser.add_argument("--scales", type=list, default=[8, 16, 32])
    parser.add_argument("--alpha", type=float, default=1.0)
    parser.add_argument("--beta", type=float, default=1.0)
    parser.add_argument("--fourier_degree", type=int, default=5)
    parser.add_argument("--det_fce_box_type", type=str, default='poly')

    # params for text recognizer
    parser.add_argument("--rec_algorithm", type=str, default='CRNN')
    parser.add_argument("--rec_model_path", type=str)
    parser.add_argument("--rec_image_inverse", type=str2bool, default=True)
    parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320")
    parser.add_argument("--rec_char_type", type=str, default='ch')
    parser.add_argument("--rec_batch_num", type=int, default=6)
    parser.add_argument("--max_text_length", type=int, default=25)

    parser.add_argument("--use_space_char", type=str2bool, default=True)
    parser.add_argument("--drop_score", type=float, default=0.5)
    parser.add_argument("--limited_max_width", type=int, default=1280)
    parser.add_argument("--limited_min_width", type=int, default=16)

    parser.add_argument(
        "--vis_font_path", type=str,
        default=os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), 'doc/fonts/simfang.ttf'))
    parser.add_argument(
        "--rec_char_dict_path",
        type=str,
        default=os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))),
                             'pytorchocr/utils/ppocr_keys_v1.txt'))

    # params for text classifier
    parser.add_argument("--use_angle_cls", type=str2bool, default=False)
    parser.add_argument("--cls_model_path", type=str)
    parser.add_argument("--cls_image_shape", type=str, default="3, 48, 192")
    parser.add_argument("--label_list", type=list, default=['0', '180'])
    parser.add_argument("--cls_batch_num", type=int, default=6)
    parser.add_argument("--cls_thresh", type=float, default=0.9)

    parser.add_argument("--enable_mkldnn", type=str2bool, default=False)
    parser.add_argument("--use_pdserving", type=str2bool, default=False)

    # params for e2e
    parser.add_argument("--e2e_algorithm", type=str, default='PGNet')
    parser.add_argument("--e2e_model_path", type=str)
    parser.add_argument("--e2e_limit_side_len", type=float, default=768)
    parser.add_argument("--e2e_limit_type", type=str, default='max')

    # PGNet parmas
    parser.add_argument("--e2e_pgnet_score_thresh", type=float, default=0.5)
    parser.add_argument(
        "--e2e_char_dict_path", type=str,
        default=os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))),
                             'pytorchocr/utils/ic15_dict.txt'))
    parser.add_argument("--e2e_pgnet_valid_set", type=str, default='totaltext')
    parser.add_argument("--e2e_pgnet_polygon", type=bool, default=True)
    parser.add_argument("--e2e_pgnet_mode", type=str, default='fast')

    # SR parmas
    parser.add_argument("--sr_model_path", type=str)
    parser.add_argument("--sr_image_shape", type=str, default="3, 32, 128")
    parser.add_argument("--sr_batch_num", type=int, default=1)

    #
    parser.add_argument("--draw_img_save_dir", type=str, default="./inference_results")
    parser.add_argument("--save_crop_res", type=str2bool, default=False)
    parser.add_argument("--crop_res_save_dir", type=str, default="./output")

    # params .yaml
    parser.add_argument("--det_yaml_path", type=str, default=None)
    parser.add_argument("--rec_yaml_path", type=str, default=None)
    parser.add_argument("--cls_yaml_path", type=str, default=None)
    parser.add_argument("--e2e_yaml_path", type=str, default=None)
    parser.add_argument("--sr_yaml_path", type=str, default=None)

    # multi-process
    parser.add_argument("--use_mp", type=str2bool, default=False)
    parser.add_argument("--total_process_num", type=int, default=1)
    parser.add_argument("--process_id", type=int, default=0)

    parser.add_argument("--benchmark", type=str2bool, default=False)
    parser.add_argument("--save_log_path", type=str, default="./log_output/")

    parser.add_argument("--show_log", type=str2bool, default=True)

    # extended function
    parser.add_argument(
        "--return_word_box",
        type=str2bool,
        default=False,
        help="Whether return the bbox of each word (split by space) or chinese character. Only used in ppstructure for layout recovery",
    )

    return parser

References