From f9a918192bad28fa53a20443b3f9fc568764e233 Mon Sep 17 00:00:00 2001 From: RangeKing Date: Tue, 28 Dec 2021 09:58:11 +0800 Subject: [PATCH] Create kie_en.md --- ppstructure/docs/kie_en.md | 77 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 77 insertions(+) create mode 100644 ppstructure/docs/kie_en.md diff --git a/ppstructure/docs/kie_en.md b/ppstructure/docs/kie_en.md new file mode 100644 index 00000000..571903ce --- /dev/null +++ b/ppstructure/docs/kie_en.md @@ -0,0 +1,77 @@ + + +# Key Information Extraction(KIE) + +This section provides a tutorial example on how to quickly use, train, and evaluate a key information extraction(KIE) model, [SDMGR](https://arxiv.org/abs/2103.14470), in PaddleOCR. + +[SDMGR(Spatial Dual-Modality Graph Reasoning)](https://arxiv.org/abs/2103.14470) is a KIE algorithm that classifies each detected text region into predefined categories, such as order ID, invoice number, amount, and etc. + + +* [1. Quick Use](#1-----) +* [2. Model Training](#2-----) +* [3. Model Evaluation](#3-----) + + + +## 1. Quick Use + +[Wildreceipt dataset](https://paperswithcode.com/dataset/wildreceipt) is used for this tutorial. It contains 1765 photos, with 25 classes, and 50000 text boxes, which can be downloaded by wget: + +``` +wget https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/wildreceipt.tar && tar xf wildreceipt.tar +``` + +Download the pretrained model and predict the result: + +``` +cd PaddleOCR/ +wget https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar && tar xf kie_vgg16.tar +python3.7 tools/infer_kie.py -c configs/kie/kie_unet_sdmgr.yml -o Global.checkpoints=kie_vgg16/best_accuracy Global.infer_img=../wildreceipt/1.txt +``` + +The prediction result is saved as the folder`./output/sdmgr_kie/predicts_kie.txt`, and the visualization result is saved as the folder`/output/sdmgr_kie/kie_results/`. + +The visualization result is shown in the figure below: + +
+ +
+ + +## 2. Model Training + +Create a softlink to the folder, `PaddleOCR/train_data`: +``` +cd PaddleOCR/ && mkdir train_data && cd train_data + +ln -s ../../wildreceipt ./ +``` + +The configuration file used for training is `configs/kie/kie_unet_sdmgr.yml`. The default training data path in the configuration file is `train_data/wildreceipt`. After preparing the data, you can execute the model training with the following command: +``` +python3.7 tools/train.py -c configs/kie/kie_unet_sdmgr.yml -o Global.save_model_dir=./output/kie/ +``` + + +## 3. Model Evaluation + +After training, you can execute the model evaluation with the following command: + +``` +python3.7 tools/eval.py -c configs/kie/kie_unet_sdmgr.yml -o Global.checkpoints=./output/kie/best_accuracy +``` + +**Reference:** + + + +```bibtex +@misc{sun2021spatial, + title={Spatial Dual-Modality Graph Reasoning for Key Information Extraction}, + author={Hongbin Sun and Zhanghui Kuang and Xiaoyu Yue and Chenhao Lin and Wayne Zhang}, + year={2021}, + eprint={2103.14470}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` -- GitLab