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README.md

-# SceneTextRemover-pytorch
+# Scene Text Remover Pytorch Implementation
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+
+This is a minimal implementation of [Scene text removal via cascaded text storke detection and erasing](https://arxiv.org/pdf/2011.09768.pdf). This   github repository is for studying on image in-painting for scene text erasing. Thank you :)
+
+
+
+## Requirements
+
+Python 3.7 or later with all [requirements.txt](./requirements.txt) dependencies installed, including `torch>=1.6`. To install run:
+
+```
+$ pip install -r requirements.txt
+```
+
+
+
+## Model Summary
+
+![model architecture](./doc/model.png)
+
+ This model has u-net like sub modules. 
+`Gd` detects text stroke image `Ms` with `I` and `M`. `G'd` detects more precise text stroke `M's`.
+Similarly,  `Gr` generates text erased image `Ite`, and `G'r` generates more precise output `I'te`.
+
+
+
+## Custom Dictionary
+
+Not to be confused, I renamed the names.
+
+`I` : Input Image (with text)
+`Mm` : Text area mask (`M` in the model)
+`Ms` : Text stroke mask; output of `Gd`
+`Ms_` : Text stroke mask; output of `G'd`
+`Msgt` : Text stroke mask ; ground truth
+`Ite` : Text erased image; output of `Gr`
+`Ite_` : Text erased image; output of `G'r`
+`Itegt`: Text erased image; ground truth
+
+
+
+## Prepare Dataset
+
+You need to prepare background images in `backs` directory and text binary images in `font_mask` directory. 
+
+![background image, text image example](./doc/back.png)
+[part of background image sample, text binary image sample]
+
+Executing `python create_dataset.py` will automatically generate `I`, `Itegt`, `Mm`, `Msgt` data.
+(If you already have `I`, `Itegt`, `Mm`, `Msgt`, you can skip this section)
+
+```
+├─dataset
+│  ├─backs
+│  │  # background images
+│  └─font_mask
+│  │  # text binary images
+│  └─train
+│  │  └─I
+│  │  └─Itegt
+│  │  └─Mm
+│  │  └─Msgt  
+│  └─val
+│     └─I
+│     └─Itegt
+│     └─Mm
+│     └─Msgt
+```
+
+I generated my dataset with 709 background images and 2410 font mask.
+I used 17040 pairs for training and 4260 pairs for validation.
+
+![](./doc/dataset_example.png)
+
+Thanks for helping me gathering background images [sina-Kim]([sina-Kim (github.com)](https://github.com/sina-Kim)).
+
+
+
+## Train
+
+All you need to do is:
+
+``` python
+python train.py
+```
+
+
+
+## Result
+
+From the left
+`I`, `Itegt`, `Ite`, `Ite_`, `Msgt`, `Ms`, `Ms_`
+
+* Epoch 2
+  ![](./doc/epoch1.png)
+
+* Epoch 5
+  ![](./doc/epoch5.png)
+* Epoch 10
+  ![](./doc/epoch10.png)
+* Epoch 30
+  ![](./doc/epoch30.png)
+
+These are not good enough for real task. I think the reason is lack of dataset and simplicity. 
+But, it was a good experience for me to implement the paper.
+
+
+
+## Issue
+
+If you are having a trouble to run this code, please use issue tab. Thank you.
+

requirements.txt

+opencv-python
+matplotlib
+numpy
+tqdm
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