1.7 KiB
1.7 KiB
PaperEdge
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
modelsdirectory. - Run the
demo.pyby 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:
