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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, in PaddleOCR.

SDMGR(Spatial Dual-Modality Graph Reasoning) 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

Wildreceipt dataset 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 ./output/sdmgr_kie/predicts_kie.txt, and the visualization results are saved in the folder/output/sdmgr_kie/kie_results/.

The visualization results are 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:

@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}
}