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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.
    • Shadow extraction:
      • Use data/MBD/infer.py (by utilizing the the mask, as shown in img1) to dewarp the original images (as shown in img2) and alb_images (as shown in img3), resulting in the dewarped images img4 and img5. Note that img1, img2, and img3 all originate from the raw data in the Doc3DShade dataset.
      • Based on img4 and img5, use data/preprocess/shadow_extract.py to extract the shadow images (as shown in img6)
    • Point self.shadow_paths in loaders/docres_loader.py to the folder containing the shadow images.
img1 img2 img3
img1 img2 img3
img4 img5 img6
img4 img5 img6
  • 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.