fireae / dewarping-document-image-by-displacement-flow-estimation

Dewarping Document Image By Displacement Flow Estimation with Fully Convolutional Network

Dewarping Document Image

Dewarping Document Image By Displacement Flow Estimation with Fully Convolutional Network.

Please browse 90_paper.pdf

Dewarping Process

We predict the displacement and the categories (foreground or background) at pixellevel by applying two tasks in FCN, and then remove the background of the input image, and mapped the foreground pixels to rectified image by interpolation according to the predicted displacements. The cracks maybe emerge in rectified image when using a forward mapping interpolation. Therefore, we construct Delaunay triangulations in all scattered pixels and then using interpolation.

Notice

• 2020.11.10 update the result file, including 6-25_11_52_54-49-rgb_ and 6-25_11_52_54-49_.

• 2022.2.17 update the Release Code.

• 2022.4.14 update Source file.

Release Code

The source code is open, please download from Source.

Sorry to be busy with master's thesis and graduation matters. Please understand that the reply is slow.

Please send an email to [email protected].

Running

1、Download model parameter and source codes

2、Resize the input image into 1024x960 (zooming in or out along the longest side and keeping the aspect ration, then filling zero for padding. )

3、Run python test.py --data_path_test=./dataset/shrink_1024_960/crop/

Training

Run python train.py

Dataset

The training dataset can be synthesised using the scripts.

The complete code will be released soon.