![](https://camo.githubusercontent.com/aee20c8bf3d57328b2a301427dac32b35bc021c15c2223ac19adfee1bab7326e/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2546302539462541342539372d4f70656e253230696e2532305370616365732d626c7565)
The code and the DIW dataset for "Learning From Documents in the Wild to Improve Document Unwarping" (SIGGRAPH 2022)
[paper]
[supplementary material]
![image](https://user-images.githubusercontent.com/12742725/177686793-77c6652e-f86a-45ea-829f-78306f2d5021.png)
Documents In the Wild (DIW) dataset (2.13GB)
link
Pretrained models (139.7MB each)
Enet
Tnet
DocUNet benchmark results
docunet_benchmark_paperedge.zip
The last row of adres.txt
is the evaluation results.
The values in the last 3 columns are AD
, MS-SSIM
, and LD
.
- Download the pretrained model to the
models
directory.
- Run the
demo.py
by the following code:
$ python demo.py --Enet_ckpt 'models/G_w_checkpoint_13820.pt' \
--Tnet_ckpt 'models/L_w_checkpoint_27640.pt' \
--img_path 'images/1.jpg' \
--out_dir 'output'
- The final result:
![compare](https://user-images.githubusercontent.com/28639377/196933170-81c7e3d8-3661-429b-ae17-efae33366545.png)