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CUTIE

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CUTIE (TensorFlow implementation of Convolutional Universal Text Information Extractor)

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# CUTIE
TensorFlow implementation of the paper "CUTIE: Learning to Understand Documents with Convolutional Universal Text Information Extractor."
Xiaohui Zhao [Paper Link](https://arxiv.org/abs/1903.12363v4)

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CUTIE 是用于“票据文档” 2D 关键信息提取/命名实体识别/槽位填充 算法。
使用CUTIE前，需先使用OCR算法对“票据文档” 中的文字执行检测和识别，而后将格式化的文本输入入CUTIE网络，具体流程可参照论文。

CUTIE can be considered as one type of 2-Dimensional Key Information Extraction, 2-D NER (Named Entity Recognition) or a 2-Dimensional 2D Slot Filling algorithm.
Before training / inference with CUTIE, prepare your structured texts in your scanned document images with any type of OCR algorithm. Refer to the CUTIE paper for details about the procedure.

### Results

Result evaluated on 4,484 receipt documents, including taxi receipts, meals entertainment receipts, and hotel receipts, with 9 different key information classes. (AP / softAP)
|Method     | #Params   |  Taxi         |  Hotel        |
| ----------|:---------:| :-----:       | :-----:       |
| CloudScan | -         |  82.0 / -     |  60.0 / -     |
| BERT      | 110M      |  88.1 / -     |  71.7 / -     |
| CUTIE     |**14M**    |**94.0 / 97.3**|**74.6 / 87.0**|

![Taxi](https://github.com/vsymbol/CUTIE/raw/master/others/example_1.jpg)

![Hotel](https://github.com/vsymbol/CUTIE/raw/master/others/example_2.jpg)

### Installation & Usage

```
pip install -r requirements.txt
```

1. Generate your own dictionary with main_build_dict.py / main_data_tokenizer.py
2. Train your model with main_train_json.py

CUTIE achieves best performance with rows/cols well configured. For more insights, refer to statistics in the file (others/TrainingStatistic.xlsx).

![Chart](https://github.com/vsymbol/CUTIE/raw/master/others/chart.jpg)

### Others

For information about the input example, refer to [issue discussion](https://github.com/vsymbol/CUTIE/issues/7).
- Apply any OCR tool that help you detecting and recognizing words in the scanned document image.
- Label image OCR results with key information class as the .json file in the invoice_data folder. (thanks to @4kssoft)

CUTIE

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