Image/Manga Translator

Translate texts in manga/images.
中文说明 | Change Log

Some manga/images will never be translated, therefore this project is born.
Primarily designed for translating Japanese text, but also support Chinese, English and Korean.
Support inpainting and text rendering.
Successor to https://github.com/PatchyVideo/MMDOCR-HighPerformance

This is a hobby project, you are welcome to contribute!
Currently this only a simple demo, many imperfections exist, we need your support to make this project better!

Support Us

GPU server is not cheap, please consider to donate to us.

• Ko-fi: https://ko-fi.com/voilelabs
• Patreon: https://www.patreon.com/voilelabs
• 爱发电: https://afdian.net/@voilelabs

Online Demo

Official Demo (by zyddnys): https://touhou.ai/imgtrans/
Browser Userscript (by QiroNT): https://greasyfork.org/scripts/437569

• Note this may not work sometimes due to stupid google gcp kept restarting my instance. In that case you can wait for me to restart the service, which may take up to 24 hrs.
• Note this online demo is using the current main branch version.

Usage

# First, you need to have Python(>=3.8) installed on your system.
$ python --version
Python 3.8.13

# Clone this repo
$ git clone https://github.com/zyddnys/manga-image-translator.git

# Install the dependencies
$ pip install -r requirements.txt

However, pydensecrf isn't listed as a dependency, so you need to install it manually.
On Windows you can download the pre-compiled wheels from https://www.lfd.uci.edu/~gohlke/pythonlibs/#_pydensecrf according to your python version and install it with pip.
On other platforms, you should be able to install it via pip install git+https://github.com/lucasb-eyer/pydensecrf.git.

Then, download ocr.ckpt, ocr-ctc.ckpt, detect.ckpt, comictextdetector.pt, comictextdetector.pt.onnx and inpainting_lama_mpe.ckpt from https://github.com/zyddnys/manga-image-translator/releases/, put them in the root directory of this repo.

[Optional if using Google translate]
Apply for Youdao or DeepL translate API, put your APP_KEY and APP_SECRET or AUTH_KEY in translators/key.py

Language Code Reference

Used by the --target-lang argument.

CHS: Chinese (Simplified)
CHT: Chinese (Traditional)
CSY: Czech
NLD: Dutch
ENG: English
FRA: French
DEU: German
HUN: Hungarian
ITA: Italian
JPN: Japanese
KOR: Korean
PLK: Polish
PTB: Portuguese (Brazil)
ROM: Romanian
RUS: Russian
ESP: Spanish
TRK: Turkish
VIN: Vietnames

Using CLI

# `--use-cuda` is optional, if you have a compatible NVIDIA GPU, you can use it.
# use `--use-inpainting` to enable inpainting.
# use `--translator=<translator>` to specify a translator.
# use `--target-lang=<languge_code>` to specify a target language.
# replace <path_to_image_file> with the path to the image file.
$ python translate_demo.py --verbose --use-inpainting --use-cuda --translator=google --target-lang=ENG --image <path_to_image_file>
# result can be found in `result/`.

CLI Batch Translation

# same options as above.
# use `--mode batch` to enable batch translation.
# replace <path_to_image_folder> with the path to the image folder.
$ python translate_demo.py --verbose --mode batch --use-inpainting --use-cuda --translator=google --target-lang=ENG --image <path_to_image_folder>
# results can be found in `<path_to_image_folder>-translated/`.

Using Browser (Web Server Mode)

# same options as above.
# use `--mode web` to start a web server.
$ python translate_demo.py --verbose --mode web --use-inpainting --use-cuda
# the demo will be serving on http://127.0.0.1:5003>

Two modes of translation service are provided by the demo: synchronous mode and asynchronous mode.
In synchronous mode your HTTP POST request will finish once the translation task is finished.
In asynchronous mode your HTTP POST request will respond with a task_id immediately, you can use this task_id to poll for translation task state.

Synchronous mode

1. POST a form request with form data file:<content-of-image> to http://127.0.0.1:5003/run
2. Wait for response
3. Use the resultant task_id to find translation result in result/ directory, e.g. using Nginx to expose result/

Asynchronous mode

1. POST a form request with form data file:<content-of-image> to http://127.0.0.1:5003/submit
2. Acquire translation task_id
3. Poll for translation task state by posting JSON {"taskid": <task-id>} to http://127.0.0.1:5003/task-state
4. Translation is finished when the resultant state is either finished, error or error-lang
5. Find translation result in result/ directory, e.g. using Nginx to expose result/

Manual translation

Manual translation replace machine translation with human translators. Basic manual translation demo can be found at http://127.0.0.1:5003/manual

POST a form request with form data file:<content-of-image> to http://127.0.0.1:5003/manual-translate and wait for response.

You will obtain a JSON response like this:

{
  "task_id": "12c779c9431f954971cae720eb104499",
  "status": "pending",
  "trans_result": [
    {
      "s": "☆上司来ちゃった……",
      "t": ""
    }
  ]
}

Fill in translated texts:

{
  "task_id": "12c779c9431f954971cae720eb104499",
  "status": "pending",
  "trans_result": [
    {
      "s": "☆上司来ちゃった……",
      "t": "☆Boss is here..."
    }
  ]
}

Post translated JSON to http://127.0.0.1:5003/post-translation-result and wait for response.
Then you can find the translation result in result/ directory, e.g. using Nginx to expose result/.

Next steps

A list of what needs to be done next, you're welcome to contribute.

1. Inpainting is based on Aggregated Contextual Transformations for High-Resolution Image Inpainting
2. IMPORTANT!!!HELP NEEDED!!! The current text rendering engine is barely usable, we need your help to improve text rendering!
3. Text rendering area is determined by detected text lines, not speech bubbles.
   This works for images without speech bubbles, but making it impossible to decide where to put translated English text. I have no idea how to solve this.
4. Ryota et al. proposed using multimodal machine translation, maybe we can add ViT features for building custom NMT models.
5. Make this project works for video(rewrite code in C++ and use GPU/other hardware NN accelerator).
   Used for detecting hard subtitles in videos, generting ass file and remove them completetly.
6. Mask refinement based using non deep learning algorithms, I am currently testing out CRF based algorithm.
7. Angled text region merge is not currently supported

Samples

The following samples are from the original version, they do not represent the current main branch version.

Original	Translated