This is a minimal implementation of Scene text removal via cascaded text stroke detection and erasing. This github repository is for studying on image in-painting for scene text erasing. Thank you :)

Python 3.7 or later with all requirements.txt dependencies installed, including torch>=1.6. To install run:

$ pip install -r requirements.txt

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.

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

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)).

All you need to do is:

python train.py

From the left I, Itegt, Ite, Ite_, Msgt, Ms, Ms_

• Epoch 2
  
• Epoch 5
  
• Epoch 10
  
• Epoch 30
  
• Epoch 50
  
• Epoch 120
  

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.

If you are having a trouble to run this code, please use issue tab. Thank you.