SAE Binarization

Repository of the Rodan wrapper for SAE Binarization

Original Code at https://github.com/ajgallego/document-image-binarization by Jorge Calvo-Zaragoza and Antonio-Javier Gallego

Python dependencies:

This background removal task belongs inside gpu-celery container.

The binarize.py script performs the binarization of an input image using a trained model. The parameters of this script are the following:

Parameter	Default	Description
`-imgpath`		Path to the image to process
`-modelpath`	(*)	Path to the model to load
`-w`	256	Input window size
`-s`	-1	Step size. -1 to use window size
`-f`	64	Number of filters
`-k`	5	Kernel size
`-drop`	0	Dropout percentage
`-stride`	2	Convolution stride size
`-every`	1	Residual connections every x layers
`-th`	0.5	Selectional threshold
`-save`		Output image filename

(*) By default, the model trained with all datasets will be used.

The only mandatory parameter is -imgpath, the rest are optional. You also have to choose if you want to save (-save) the binarized image.

For example, to binarize the image img01.png you can run the following command:

$ python binarize.py -imgpath img01.png -save out.png