Awesome
PaperEdge
<a href="https://huggingface.co/spaces/SWHL/PaperEdgeDemo"><img src="https://img.shields.io/badge/%F0%9F%A4%97-Open%20in%20Spaces-blue"></a><br/> The code and the DIW dataset for "Learning From Documents in the Wild to Improve Document Unwarping" (SIGGRAPH 2022)
[paper] [supplementary material]
Documents In the Wild (DIW) dataset (2.13GB)
Pretrained models (139.7MB each)
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
.
Infer one image.
- 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: