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Currently, I am still working on the SickZil-Machine. It is just laying the groundwork invisible to users. I'm building the system I need for more effective training data collection, more efficient deep learning model training, faster experimentation and analysis, model serving and deployment automation, etc.
I will go to the distance. I appreciate if you could wait a little longer. Thank you.
(source: manga109, © Kanno Hiroshi, © Okuda Momoko, © Kato Masaki)
SickZil-Machine automates texts removal during manga/comics translation(Scanlation) process.
All of the above images were edited automatically without human intervention.
(source: manga109, © Shimazaki Yuzuru, © Akamatsu Ken, © Kobayashi Yuki)
SickZil-Machine finds out the texts in manga/comics and erases it naturally to match the background.
Both processes are completely automatic, without any additional human intervention.
Of course, if you want, you can also specify text area you want to erase.
By the way, SickZil is korean word 식질, slang of 식자(작업). 식자 means editing manga/comics according to the translation(from translator).
SickZil-Machine consists of two deep learning models, SegNet and ComplNet.
To learn SegNet, we need original manga images and
text component masks that cover all text area corresponding to the original images.
To learn ComplNet, we need manga images with text removed (ie output).
(I'm researching how an images with a small amount of text affects performance.
manga images with no text at all are the ideal data.)
Version 0.1.1 was trained using 285 image-mask pairs and 31,497 manga images.
(11,464 of 31,497 manga images are images with text.)
If you'd like to contribute a dataset to SickZil-Machine, please send your data to email .
The dataset will only be used for research purposes.
You need NVIDIA driver 410.x, CUDA 10.0, CUDNN (>= 7.4.1). (tensorflow 1.13.0 requirements)
git clone https://github.com/KUR-creative/SickZil-Machine.git; cd SickZil-Machine
pip install -r requirements.txt
cd src; python main.py