@@ -8,7 +8,6 @@ PP-Structure is an OCR toolkit that can be used for complex documents analysis.
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@@ -8,7 +8,6 @@ PP-Structure is an OCR toolkit that can be used for complex documents analysis.
- Support to extract excel files from the table areas
- Support to extract excel files from the table areas
- Support python whl package and command line usage, easy to use
- Support python whl package and command line usage, easy to use
- Support custom training for layout analysis and table structure tasks
- Support custom training for layout analysis and table structure tasks
- The total model size is only about 18.6M (continuous optimization)
## 1. Visualization
## 1. Visualization
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@@ -187,4 +186,4 @@ OCR and table recognition model
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@@ -187,4 +186,4 @@ OCR and table recognition model
|en_ppocr_mobile_v2.0_table_rec|Text recognition of English table scene trained on PubLayNet dataset|6.9M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar) |
|en_ppocr_mobile_v2.0_table_rec|Text recognition of English table scene trained on PubLayNet dataset|6.9M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar) |
|en_ppocr_mobile_v2.0_table_structure|Table structure prediction of English table scene trained on PubLayNet dataset|18.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) |
|en_ppocr_mobile_v2.0_table_structure|Table structure prediction of English table scene trained on PubLayNet dataset|18.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) |
If you need to use other models, you can download the model in [model_list](../doc/doc_en/models_list_en.md) or use your own trained model to configure it to the three fields of `det_model_dir`, `rec_model_dir`, `table_model_dir` .
If you need to use other models, you can download the model in [model_list](../doc/doc_en/models_list_en.md) or use your own trained model to configure it to the three fields of `det_model_dir`, `rec_model_dir`, `table_model_dir` .