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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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eval

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networks

 
 

utils

utils

 
 

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README.md

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eval.py

eval.py

 
 

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eval.sh

 
 

train.py

train.py

 
 

train.sh

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PaperEdge

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

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Documents In the Wild (DIW) dataset (2.13GB)

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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.

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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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