112 lines
3.9 KiB
Markdown
112 lines
3.9 KiB
Markdown
# Manga OCR
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Optical character recognition for Japanese text, with the main focus being Japanese manga.
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It uses a custom end-to-end model built with Transformers' [Vision Encoder Decoder](https://huggingface.co/docs/transformers/model_doc/visionencoderdecoder) framework.
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Manga OCR can be used as a general purpose printed Japanese OCR, but its main goal was to provide a high quality
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text recognition, robust against various scenarios specific to manga:
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- both vertical and horizontal text
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- text with furigana
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- text overlaid on images
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- wide variety of fonts and font styles
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- low quality images
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Unlike many OCR models, Manga OCR supports recognizing multi-line text in a single forward pass,
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so that text bubbles found in manga can be processed at once, without splitting them into lines.
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# Installation
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You need Python 3.6+.
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If you want to run with GPU, install PyTorch as described [here](https://pytorch.org/get-started/locally/#start-locally),
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otherwise this step can be skipped.
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Run:
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```commandline
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pip install manga-ocr
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```
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# Usage
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## Running in the background
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Manga OCR can run in the background, processing new images as they appear.
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You might then use a tool like [ShareX](https://getsharex.com/) to manually capture a region of the screen and let the
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OCR read it either from the system clipboard, or a specified directory.
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For example:
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- To read images from clipboard and write recognized texts to clipboard, run:
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```commandline
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manga_ocr
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```
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- To read images from ShareX's screenshot folder, run:
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```commandline
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manga_ocr "/path/to/sharex/screenshot/folder"
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```
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- To see other options, run:
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```commandline
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manga_ocr --help
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```
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If `manga_ocr` doesn't work, you might also try replacing it with `python -m manga_ocr`.
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## Python API
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```python
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from manga_ocr import MangaOcr
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mocr = MangaOcr()
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text = mocr('/path/to/img')
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```
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or
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```python
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import PIL.Image
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from manga_ocr import MangaOcr
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mocr = MangaOcr()
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img = PIL.Image.open('/path/to/img')
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text = mocr(img)
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```
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## Usage tips
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- OCR supports multi-line text, but the longer the text, the more likely some errors are to occur.
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If the recognition failed for some part of a longer text, you might try to run it on a smaller portion of the image.
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- The model was trained specifically to handle manga well, but should do a decent job on other types of printed text,
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such as novels or video games. It probably won't be able to handle handwritten text though.
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- The model always attempts to recognize some text on the image, even if there is none.
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Because it uses a transformer decoder (and therefore has some understanding of the Japanese language),
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it might even "dream up" some realistically looking sentences! This shouldn't be a problem for most use cases,
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but it might get improved in the next version.
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# Examples
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Here are some cherry-picked examples showing the capability of the model.
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| image | Manga OCR result |
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|----------------------|------------------|
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|  | 素直にあやまるしか |
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|  | 立川で見た〝穴〟の下の巨大な眼は： |
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|  | 実戦剣術も一流です |
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|  | 第３０話重苦しい闇の奥で静かに呼吸づきながら |
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|  | よかったじゃないわよ！何逃げてるのよ！！早くあいつを退治してよ！ |
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|  | ぎゃっ |
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|  | ピンポーーン |
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|  | ＬＩＮＫ！私達７人の力でガノンの塔の結界をやぶります |
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|  | ファイアパンチ |
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|  | 少し黙っている |
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|  | わかるかな〜？ |
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|  | 警察にも先生にも町中の人達に！！ |
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# Acknowledgments
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This project was done with the usage of [Manga109-s](http://www.manga109.org/en/download_s.html) dataset.
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