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The code and the DIW dataset for "Learning From Documents in the Wild to Improve Document Unwarping" (SIGGRAPH 2022)

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PaperEdge


The code and the DIW dataset for "Learning From Documents in the Wild to Improve Document Unwarping" (SIGGRAPH 2022)

[paper] [supplementary material] image

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.

Infer one image.

  1. Download the pretrained model to the models directory.
  2. 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'
  3. The final result: compare

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The code and the DIW dataset for "Learning From Documents in the Wild to Improve Document Unwarping" (SIGGRAPH 2022)

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  • Python 92.0%
  • MATLAB 7.8%
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