# KIE Algorithm - VI-LayoutXLM


- [1. Introduction](#1-introduction)
- [2. Environment](#2-environment)
- [3. Model Training / Evaluation / Prediction](#3-model-training--evaluation--prediction)
- [4. Inference and Deployment](#4-inference-and-deployment)
  - [4.1 Python Inference](#41-python-inference)
  - [4.2 C++ Inference](#42-c-inference)
  - [4.3 Serving](#43-serving)
  - [4.4 More](#44-more)
- [5. FAQ](#5-faq)
- [Citation](#Citation)


## 1. Introduction

VI-LayoutXLM is improved based on LayoutXLM. In the process of downstream finetuning, the visual backbone network module is removed, and the model infernce speed is further improved on the basis of almost lossless accuracy.

On XFUND_zh dataset, the algorithm reproduction Hmean is as follows.

|Model|Backbone|Task |Cnnfig|Hmean|Download link|
| --- | --- |---| --- | --- | --- |
|VI-LayoutXLM |VI-LayoutXLM-base | SER |[ser_vi_layoutxlm_xfund_zh_udml.yml](../../configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh_udml.yml)|93.19%|[trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_pretrained.tar)/[inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_infer.tar)|
|VI-LayoutXLM |VI-LayoutXLM-base |RE | [re_vi_layoutxlm_xfund_zh_udml.yml](../../configs/kie/vi_layoutxlm/re_vi_layoutxlm_xfund_zh_udml.yml)|83.92%|[trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_pretrained.tar)/[inference model(coming soon)]()|


Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code.


## 3. Model Training / Evaluation / Prediction

Please refer to [KIE tutorial](./kie_en.md)。PaddleOCR has modularized the code structure, so that you only need to **replace the configuration file** to train different models.


## 4. Inference and Deployment

### 4.1 Python Inference

**Note:** Currently, the RE model inference process is still in the process of adaptation. We take SER model as an example to introduce the KIE process based on VI-LayoutXLM model.

First, we need to export the trained model into inference model. Take VI-LayoutXLM model trained on XFUND_zh as an example ([trained model download link](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_pretrained.tar)). Use the following command to export.


``` bash
wget https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_pretrained.tar
tar -xf ser_vi_layoutxlm_xfund_pretrained.tar
python3 tools/export_model.py -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./ser_vi_layoutxlm_xfund_pretrained/best_accuracy Global.save_inference_dir=./inference/ser_vi_layoutxlm_infer
```

Use the following command to infer using VI-LayoutXLM SER model.


```bash
cd ppstructure
python3 kie/predict_kie_token_ser.py \
  --kie_algorithm=LayoutXLM \
  --ser_model_dir=../inference/ser_vi_layoutxlm_infer \
  --image_dir=./docs/kie/input/zh_val_42.jpg \
  --ser_dict_path=../train_data/XFUND/class_list_xfun.txt \
  --vis_font_path=../doc/fonts/simfang.ttf \
  --ocr_order_method="tb-yx"
```

The SER visualization results are saved in the `./output` folder by default. The results are as follows.


<div align="center">
    <img src="../../ppstructure/docs/kie/result_ser/zh_val_42_ser.jpg" width="800">
</div>


### 4.2 C++ Inference

Not supported

### 4.3 Serving

Not supported

### 4.4 More

Not supported

## 5. FAQ

## Citation


```bibtex
@article{DBLP:journals/corr/abs-2104-08836,
  author    = {Yiheng Xu and
               Tengchao Lv and
               Lei Cui and
               Guoxin Wang and
               Yijuan Lu and
               Dinei Flor{\^{e}}ncio and
               Cha Zhang and
               Furu Wei},
  title     = {LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich
               Document Understanding},
  journal   = {CoRR},
  volume    = {abs/2104.08836},
  year      = {2021},
  url       = {https://arxiv.org/abs/2104.08836},
  eprinttype = {arXiv},
  eprint    = {2104.08836},
  timestamp = {Thu, 14 Oct 2021 09:17:23 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-08836.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-1912-13318,
  author    = {Yiheng Xu and
               Minghao Li and
               Lei Cui and
               Shaohan Huang and
               Furu Wei and
               Ming Zhou},
  title     = {LayoutLM: Pre-training of Text and Layout for Document Image Understanding},
  journal   = {CoRR},
  volume    = {abs/1912.13318},
  year      = {2019},
  url       = {http://arxiv.org/abs/1912.13318},
  eprinttype = {arXiv},
  eprint    = {1912.13318},
  timestamp = {Mon, 01 Jun 2020 16:20:46 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1912-13318.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2012-14740,
  author    = {Yang Xu and
               Yiheng Xu and
               Tengchao Lv and
               Lei Cui and
               Furu Wei and
               Guoxin Wang and
               Yijuan Lu and
               Dinei A. F. Flor{\^{e}}ncio and
               Cha Zhang and
               Wanxiang Che and
               Min Zhang and
               Lidong Zhou},
  title     = {LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding},
  journal   = {CoRR},
  volume    = {abs/2012.14740},
  year      = {2020},
  url       = {https://arxiv.org/abs/2012.14740},
  eprinttype = {arXiv},
  eprint    = {2012.14740},
  timestamp = {Tue, 27 Jul 2021 09:53:52 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2012-14740.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```