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Optical character recognition for Japanese text, with the main focus being Japanese manga
| Date | Stars |
|---|---|
| 2026-07-24 | 2726 |
| 2026-07-25 | 2726 |
| 2026-07-28 | 2728 |
| 2026-07-30 | 2728 |
| 2026-08-06 | 2728 |
Today
— stars today
This week
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This month
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Momentum
35.0
growth rate 0.00%/day
# Manga OCR
Optical character recognition for Japanese text, with the main focus being Japanese manga.
It uses a custom end-to-end model built with Transformers' [Vision Encoder Decoder](https://huggingface.co/docs/transformers/model_doc/vision-encoder-decoder) framework.
Manga OCR can be used as a general purpose printed Japanese OCR, but its main goal was to provide a high quality
text recognition, robust against various scenarios specific to manga:
- both vertical and horizontal text
- text with furigana
- text overlaid on images
- wide variety of fonts and font styles
- low quality images
Unlike many OCR models, Manga OCR supports recognizing multi-line text in a single forward pass,
so that text bubbles found in manga can be processed at once, without splitting them into lines.
See also:
- [Poricom](https://github.com/bluaxees/Poricom), a GUI reader, which uses manga-ocr
- [mokuro](https://github.com/kha-white/mokuro), a tool, which uses manga-ocr to generate an HTML overlay for manga
- [Xelieu's guide](https://rentry.co/lazyXel), a comprehensive guide on setting up a reading and mining workflow with manga-ocr/mokuro (and many other useful tips)
- Development code, including code for training and synthetic data generation: [link](manga_ocr_dev)
- Description of synthetic data generation pipeline + examples of generated images: [link](manga_ocr_dev/synthetic_data_generator)
# Installation
You need Python 3.9 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.
Install with pip:
```commandline
pip install manga-ocr
```
Or with [uv](https://docs.astral.sh/uv/):
```commandline
uv add manga-ocr
```
You can also run manga-ocr without installing it into your project, using `uvx`:
```commandline
uvx manga_ocr
```
## Troubleshooting
- `ImportError: DLL load failed while importing fugashi: The specified module could not be found.` - might be because of Python installed from Microsoft Store, try installing Python from the [official site](https://www.python.org/downloads)
- problem with installing `mecab-python3` on ARM architecture - try [this workaround](https://github.com/kha-white/manga-ocr/issues/16)
# Usage
## Python API
```python
from manga_ocr import MangaOcr
mocr = MangaOcr()
text = mocr('/path/to/img')
```
or
```python
import PIL.Image
from manga_ocr import MangaOcr
mocr = MangaOcr()
img = PIL.Image.open('/path/to/img')
text = mocr(img)
```
## Running in the background
Manga OCR can run in the background and process new images as they appear.
You might use a tool like [ShareX](https://getsharex.com/) or [Flameshot](https://flameshot.org/) to manually capture a region of the screen and let the
OCR read it either from the system clipboard, or a specified directory. By default, Manga OCR will write recognized text to clipboard,
from which it can be read by a dictionary like [Yomitan](https://github.com/yomidevs/yomitan).
Clipboard mode on Linux requires `wl-copy` for Wayland sessions or `xclip` for X11 sessions. You can find out which one your system needs by running `echo $XDG_SESSION_TYPE` in the terminal.
Your full setup for reading manga in Japanese with a dictionary might look like this:
capture region with ShareX -> write image to clipboard -> Manga OCR -> write text to clipboard -> Yomitan
https://user-images.githubusercontent.Excerpt of 6,899 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e3a6f1b404d3a254, topic:ocr, name:ocr, readme:ocr
matched fp:e3a6f1b404d3a254, topic:deep-learning
matched fp:e3a6f1b404d3a254, topic:computer-vision
