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Read Japanese manga inside browser with selectable text.
| Date | Stars |
|---|---|
| 2026-07-24 | 1677 |
| 2026-07-25 | 1677 |
| 2026-07-28 | 1677 |
| 2026-07-30 | 1677 |
| 2026-08-06 | 1677 |
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# mokuro Read Japanese manga with selectable text inside a browser. **See demo: https://kha-white.github.io/manga-demo** https://user-images.githubusercontent.com/22717958/164993274-3e8d1650-9be3-457d-84cb-f92f9598cd5a.mp4 <sup>Demo contains excerpt from [Manga109-s dataset](http://www.manga109.org/en/download_s.html). うちの猫’ず日記 © がぁさん</sup> mokuro is aimed towards Japanese learners, who want to read manga in Japanese with a pop-up dictionary like [Yomitan](https://github.com/themoeway/yomitan). It works like this: 1. Perform text detection and OCR for each page. 2. After processing a whole volume, generate a .mokuro file, which contains OCR results and metadata. All processing is done offline (before reading). 3. Load the .mokuro file together with manga images in [web reader](https://reader.mokuro.app/), which serves both as a manga reader and a catalog for processed series and volumes. Alternatively, you can still use the old method from mokuro 0.1.*: Instead of a .mokuro file, generate an HTML file, which you can open in a browser. You can transfer the resulting HTML file together with manga images to another device (e.g. your mobile phone) and read there. This method is still supported for backward compatibility, but it is recommended to use the new .mokuro format and the web reader. For details, see [Legacy HTML vs. new .mokuro format](#legacy-html-vs-new-mokuro-format). mokuro uses [comic-text-detector](https://github.com/dmMaze/comic-text-detector) for text detection and [manga-ocr](https://github.com/kha-white/manga-ocr) for OCR. Try running on your manga in Colab: [](https://colab.research.google.com/github/kha-white/mokuro/blob/master/notebooks/mokuro_demo.ipynb) See also: - [mokuro-reader](https://github.com/Gnathonic/mokuro-reader), a web reader for mokuro, developed now by Gnathonic and formerly by ZXY101 - [Mokuro2Pdf](https://github.com/Kartoffel0/Mokuro2Pdf), cli Ruby script to generate pdf files with selectable text from Mokuro's html overlay - [Xelieu's guide](https://lazyguidejp.github.io/jp-lazy-guide/setupMangaOnPC/), a comprehensive guide on setting up a reading and mining workflow with manga-ocr/mokuro (and many other useful tips) # Installation You need Python 3.10 or newer. Please note, that the newest Python release might not be supported due to a PyTorch dependency, which often breaks with new Python releases and needs some time to catch up. Refer to [PyTorch website](https://pytorch.org/get-started/locally/) for a list of supported Python versions. Some users have reported problems with Python installed from Microsoft Store. If you see an error: `ImportError: DLL load failed while importing fugashi: The specified module could not be found.`, try installing Python from the [official site](https://www.python.org/downloads). If you want to run with GPU, install PyTorch as described [here](https://pytorch.org/get-started/locally/#start-locally), otherwise this step can be skipped. Run in command line: ```commandline pip3 install mokuro ``` # Usage ## Run on one volume ```bash mokuro /path/to/manga/vol1 ``` This will generate `/path/to/manga/vol1.html` file, which you can open in a browser. If your path contains spaces, enclose it in double quotes, like this: ```bash mokuro "/path/to/manga/volume 1" ``` ## Run on multiple volumes ```bash mokuro /path/to/manga/vol1 /path/to/manga/vol2 /path/to/manga/vol3 ``` For each volume, a separate HTML file will be generated. ## Run on a directory containing multiple volumes If your directory structure looks somewhat like this: ``` manga_title/ ├─vol1/ ├─vol2/ ├─vol3/ └─vol4/ ``` You can process all volumes by running: ```bash mokuro --parent_dir manga_title/ ``` ## Other options ``` --pretrained_model_name_or_path: Name or path of the manga-ocr model. --force_cpu: Force the use of CPU even if CUDA is available. --disable_confirmation: Disable confirmation prompt.
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:47eb86800a5485b2, topic:ocr, readme:ocr