SDSVKIE

<p align="center">
  <h1 align="center">SDSVKIE</h1>
</p>

***Feature***
- Extract information from documents: VAT Invoice, Receipt
- Language: VI + EN

***What's news***
### - Ver 1.0.1:
- Improve postprocessing for receipts
- Support handling multiple pages for PDF files
- Lastest weight: /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_9_lr5e_6_no_scheduler/best
- Lastest config: /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_9_lr5e_6_no_scheduler/config.yaml


## I. Setup 
***Dependencies***
- Python: 3.8
- Torch: 1.10.2
- CUDA: 11.6
- transformers: 4.28.1
```
pip install -v -e .
```


## II. Inference
```
from sdsvkie import Predictor
import cv2 

predictor = Predictor(
    cfg="./workdirs/training/sdsap_receipt/exp_3/config.yaml", 
    weights="./workdirs/training/sdsap_receipt/exp_3/best",
    device="cpu",
)
img = cv2.imread("./demos/4 Sep OPC to Home.jpg")
out = predictor(img)
output = out['end2end_results']

```


## III. Training
- Prepare dataset: The structure of the dataset directory is organized as follows:

└── base_dataset \
  ├── train \
  ├──── sub_dir_1 \
  ├────── img1.txt \
  ├────── img1.txt \
  ├────── ... \
  ├──── sub_dir_2 \
  ├────── img2.txt \
  ├────── img2.txt \
  ├── test \
  ├──── imgn.jpg \
  ├──── imgn.txt

- Edit and run scripts:
```
sh ./scripts/train.sh
```

# TODO

- [ ] Add more fields: sub_total, tips, seller_address, item list
- [x] Support muliple pages
- [x] Review result KIE for invoice (vnpt_exp_4_model)
- [x] Fix unnormalize box error in some cases
- [x] Support multiple pages
- [x] Create 200 multiple pages invoice
- [ ] Finalize multi page testset
- [ ] Eval result