这是通过级联文本笔画检测和擦除来删除场景文本的最小实现。这个github存储库用于研究场景文本擦除的图像修复。谢谢：）.zip

# Scene Text Remover Pytorch Implementation This is a minimal implementation of [Scene text removal via cascaded text stroke 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  This model has u-net 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)<br> `Mm` : Text area mask (`M` in the model)<br> `Ms` : Text stroke mask; output of `Gd`<br> `Ms_` : Text stroke mask; output of `G'd`<br> `Msgt` : Text stroke mask ; ground truth<br> `Ite` : Text erased image; output of `Gr`<br> `Ite_` : Text erased image; output of `G'r`<br> `Itegt`: Text erased image; ground truth<br> ## Prepare Dataset You need to prepare background images in `backs` directory and text binary images in `font_mask` directory.  [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.  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<br>  * Epoch 5<br>  * Epoch 10<br>  * Epoch 30<br>  * Epoch 50<br>  * Epoch 120<br>  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.