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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](https://github.com/ZZZHANG-jx/Recommendations-Document-Image-Processing/tree/master) 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](https://github.com/ZZZHANG-jx/Recommendations-Document-Image-Processing/tree/master) 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](https://www.robots.ox.ac.uk/~vgg/data/dtd/) 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:
+        - Run `python data/preprocess/shadow_extraction.py` to extract shadows. 
+        - The principle of  `data/preprocess/shadow_extraction.py`:
+            - step1: utilize 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
+            - step2: extract the shadow images (as shown in img6) based on img4 and img5.
+    - Point self.shadow_paths in `loaders/docres_loader.py` to the folder containing the shadow images.
+
+|img1|img2|img3|
+|----|----|----|
+|![img1](./images/1.png)|![img2](./images/2.png)|![img3](./images/3.png)|
+|img4|img5|img6|
+|![img4](./images/4.png)|![img5](./images/5.png)|![img6](./images/6.png)|
+
+- 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`.
+
+
+