Dataset Preparation

The data files tree should be look like:

data/
    eval/
        dir300/
            1_in.png
            1_gt.png
            ...
        kligler/
        jung/
        osr/
        realdae/
        docunet_docaligner/
        dibco18/
    train/
        dewarping/
            doc3d/
        deshadowing/
            fsdsrd/
            tdd/
        appearance/
            clean_pdfs/
            realdae/
        deblurring/
            tdd/
        binarization/
            bickly/
            dibco/
            noise_office/
            phibd/
            msi/

Evaluation Dataset

You can find the links for downloading the dataset we used for evaluation (Tables 1 and 2) in this repository, including DIR300 (300 samples), Kligler (300 samples), Jung (87 samples), OSR (237 samples), RealDAE (150 samples), DocUNet_DocAligner (150 samples), TDD (16000 samples) and DIBCO18 (10 samples). After downloading, add the suffix of _in and _gt to the input image and gt image respectively, and place them in the folder of the corresponding dataset

Training Dataset

You can find the links for downloading the dataset we used for training in this repository.

Dewarping

Doc3D
- Mask extraction: you should extract the mask for each image from the uv data in Doc3D
- Background preparation: you can download the background data from here and specify it for self.background_paths in loaders/docres_loader.py
JSON preparation:


[
    ## you need to specify the paths of 'in_path', 'mask_path and 'gt_path':
    {
        "in_path": "dewarping/doc3d/img/1/102_1-pp_Page_048-xov0001.png",
        "mask_path": "dewarping/doc3d/mask/1/102_1-pp_Page_048-xov0001.png",
        "gt_path": "dewarping/doc3d/bm/1/102_1-pp_Page_048-xov0001.npy"
    }
]

Deshadowing

RDD
FSDSRD
JSON preparation

[   ## you need to specify the paths of 'in_path' and 'gt_path', for example:
    {
        "in_path": "deshadowing/fsdsrd/im/00004.png",
        "gt_path": "deshadowing/fsdsrd/gt/00004.png"
    },
    {
        "in_path": "deshadowing/rdd/im/00004.png",
        "gt_path": "deshadowing/rdd/gt/00004.png"
    }
]

Appearance enhancement

Doc3DShade
- Clean PDFs collection: You should collection PDFs files from the internet and convert them as images to serve as the source for synthesis.
- Extract shadows from Doc3DShade by using data/preprocess/shadow_extract.py and dewarp the obtained shadows by using data/MBD/infer.py. Then you should specify self.shadow_paths in loaders/docres_loader.py
RealDAE
JSON preparation:

[
    ## for Doc3DShade dataset, you only need to specify the path of image from PDF, for example:
    {   
        'gt_path':'appearance/clean_pdfs/1.jpg'
    },

    ## for RealDAE dataset, you need to specify the paths of both input and gt, for example:
    {
        'in_path': 'appearance/realdae/1_in.jpg',
        'gt_path': 'appearance/realdae/1_gt.jpg'
    }
]

Debluring

TDD
JSON preparation

[   ## you need to specify the paths of 'in_path' and 'gt_path', for example:
    {
        "in_path": "debluring/tdd/im/00004.png",
        "gt_path": "debluring/tdd/gt/00004.png"
    },
]

Binarization

Bickly
- DTPrompt preparation: Since the DTPrompt for binarization is time-expensive, we obtain it offline before training. Use data/preprocess/sauvola_binarize.py
DIBCO
- DTPrompt preparation: the same as Bickly
Noise Office
- DTPrompt preparation: the same as Bickly
PHIDB
- DTPrompt preparation: the same as Bickly
MSI
- DTPrompt preparation: the same as Bickly
JSON preparation

[   
    ## you need to specify the paths of 'in_path', 'gt_path', 'bin_path', 'thr_path' and 'gradient_path', for example:
    {
        "in_path": "binarization/noise_office/imgs/1.png",
        "gt_path": "binarization/noise_office/gt_imgs/1.png",
        "bin_path": "binarization/noise_office/imgs/1_bin.png",
        "thr_path": "binarization/noise_office/imgs/1_thr.png",
        "gradient_path": "binarization/noise_office/imgs/1_gradient.png"
    },
]

After all the data are prepared, you should specify the dataset_setting in train.py.