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PP-Structure Model list

1. Layout Analysis

model name description inference model size download dict path
picodet_lcnet_x1_0_fgd_layout The layout analysis English model trained on the PubLayNet dataset based on PicoDet LCNet_x1_0 and FGD . the model can recognition 5 types of areas such as Text, Title, Table, Picture and List 9.7M inference model / trained model PubLayNet dict
ppyolov2_r50vd_dcn_365e_publaynet The layout analysis English model trained on the PubLayNet dataset based on PP-YOLOv2 221.0M inference_moel / trained model same as above
picodet_lcnet_x1_0_fgd_layout_cdla The layout analysis Chinese model trained on the CDLA dataset, the model can recognition 10 types of areas such as Table、Figure、Figure caption、Table、Table caption、Header、Footer、Reference、Equation 9.7M inference model / trained model CDLA dict
picodet_lcnet_x1_0_fgd_layout_table The layout analysis model trained on the table dataset, the model can detect tables in Chinese and English documents 9.7M inference model / trained model Table dict
ppyolov2_r50vd_dcn_365e_tableBank_word The layout analysis model trained on the TableBank Word dataset based on PP-YOLOv2, the model can detect tables in English documents 221.0M inference model same as above
ppyolov2_r50vd_dcn_365e_tableBank_latex The layout analysis model trained on the TableBank Latex dataset based on PP-YOLOv2, the model can detect tables in English documents 221.0M inference model same as above

2. OCR and Table Recognition

2.1 OCR

model name description inference model size download
en_ppocr_mobile_v2.0_table_det Text detection model of English table scenes trained on PubTabNet dataset 4.7M inference model / trained model
en_ppocr_mobile_v2.0_table_rec Text recognition model of English table scenes trained on PubTabNet dataset 6.9M inference model / trained model

If you need to use other OCR models, you can download the model in PP-OCR model_list or use the model you trained yourself to configure to det_model_dir, rec_model_dir field.

2.2 Table Recognition

model description inference model size download
en_ppocr_mobile_v2.0_table_structure English table recognition model trained on PubTabNet dataset based on TableRec-RARE 6.8M inference model / trained model
en_ppstructure_mobile_v2.0_SLANet English table recognition model trained on PubTabNet dataset based on SLANet 9.2M inference model / trained model
ch_ppstructure_mobile_v2.0_SLANet Chinese table recognition model based on SLANet 9.3M inference model / trained model

3. KIE

On XFUND_zh dataset, Accuracy and time cost of different models on V100 GPU are as follows.

Model Backbone Task Config Hmean Time cost(ms) Download link
VI-LayoutXLM VI-LayoutXLM-base SER ser_vi_layoutxlm_xfund_zh_udml.yml 93.19% 15.49 trained model
LayoutXLM LayoutXLM-base SER ser_layoutxlm_xfund_zh.yml 90.38% 19.49 trained model
LayoutLM LayoutLM-base SER ser_layoutlm_xfund_zh.yml 77.31% - trained model
LayoutLMv2 LayoutLMv2-base SER ser_layoutlmv2_xfund_zh.yml 85.44% 31.46 trained model
VI-LayoutXLM VI-LayoutXLM-base RE re_vi_layoutxlm_xfund_zh_udml.yml 83.92% 15.49 trained model
LayoutXLM LayoutXLM-base RE re_layoutxlm_xfund_zh.yml 74.83% 19.49 trained model
LayoutLMv2 LayoutLMv2-base RE re_layoutlmv2_xfund_zh.yml 67.77% 31.46 trained model
  • Note: The above time cost information just considers inference time without preprocess or postprocess, test environment: V100 GPU + CUDA 10.2 + CUDNN 8.1.1 + TRT 7.2.3.4

On wildreceipt dataset, the algorithm result is as follows:

Model Backbone Config Hmean Download link
SDMGR VGG6 configs/kie/sdmgr/kie_unet_sdmgr.yml 86.70% trained model