From 5e6b1b2f14a1d9b72e928b728204d5658a45c3a1 Mon Sep 17 00:00:00 2001 From: kotaro-kinoshita Date: Tue, 26 Nov 2024 16:17:23 +0900 Subject: [PATCH 1/3] add english document --- README.md | 2 + README_EN.md | 93 +++++ docs/index.en.md | 21 + docs/{index.md => index.ja.md} | 14 - docs/installation.en.md | 47 +++ docs/{installation.md => installation.ja.md} | 13 +- docs/usage.en.md | 223 ++++++++++ docs/{usage.md => usage.ja.md} | 0 mkdocs.yml | 14 +- pyproject.toml | 9 +- uv.lock | 415 +++++++------------ 11 files changed, 572 insertions(+), 279 deletions(-) create mode 100644 README_EN.md create mode 100644 docs/index.en.md rename docs/{index.md => index.ja.md} (82%) create mode 100644 docs/installation.en.md rename docs/{installation.md => installation.ja.md} (65%) create mode 100644 docs/usage.en.md rename docs/{usage.md => usage.ja.md} (100%) diff --git a/README.md b/README.md index 1f87dfc..18c88c2 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,5 @@ +日本語版 | [English](README_EN.md) + ![Python](https://img.shields.io/badge/Python-3.9|3.10|3.11|3.12-F9DC3E.svg?logo=python&logoColor=&style=flat) diff --git a/README_EN.md b/README_EN.md new file mode 100644 index 0000000..3e90a6e --- /dev/null +++ b/README_EN.md @@ -0,0 +1,93 @@ +[日本語版](README.md) | English + + + +![Python](https://img.shields.io/badge/Python-3.9|3.10|3.11|3.12-F9DC3E.svg?logo=python&logoColor=&style=flat) +![Pytorch](https://img.shields.io/badge/Pytorch-2.5-EE4C2C.svg?logo=Pytorch&style=fla) +![CUDA](https://img.shields.io/badge/CUDA->=11.8-76B900.svg?logo=NVIDIA&style=fla) +![OS](https://img.shields.io/badge/OS-Linux|Mac|Win-1793D1.svg?&style=fla) +[![Document](https://img.shields.io/badge/docs-live-brightgreen)](https://kotaro-kinoshita.github.io/yomitoku-dev/) + +## 🌟 Introduction + +YomiToku is a Document AI engine specialized in Japanese document image analysis. It provides full OCR (optical character recognition) and layout analysis capabilities, enabling the recognition, extraction, and conversion of text and diagrams from images. + +- 🤖 Equipped with four AI models trained on Japanese datasets: text detection, text recognition, layout analysis, and table structure recognition. All models are independently trained and optimized for Japanese documents, delivering high-precision inference. +- 🇯🇵 Each model is specifically trained for Japanese document images, supporting the recognition of over 7,000 Japanese characters, including vertical text and other layout structures unique to Japanese documents. (It also supports English documents.) +- 📈 By leveraging layout analysis, table structure parsing, and reading order estimation, it extracts information while preserving the semantic structure of the document layout. +- 📄 Supports a variety of output formats, including HTML, Markdown, JSON, and CSV. It also allows for the extraction of diagrams and images contained within the documents. +- ⚡ Operates efficiently in GPU environments, enabling fast document transcription and analysis. It requires less than 8GB of VRAM, eliminating the need for high-end GPUs. + +## 🖼️ Demo + +The verification results for various types of images are also included in [gallery.md](gallery.md) + +| Input | Results of OCR | +| :--------------------------------------------------------: | :-----------------------------------------------------: | +| | | +| Results of Layout Analysis | Results of HTML Export | +| | | + + +For the results exported in Markdown, please refer to [static/out/in_demo_p1.md](static/out/in_demo_p1.md) in the repository. + +- `Red Frame`: Positions of figures and images +- `Green Frame`: Overall table region +- `Pink Frame`:` Table cell structure (text within the cells represents [row number, column number] (rowspan x colspan)) +- `Blue Frame`: Paragraph and text group regions +- `Red Arrow`: Results of reading order estimation + +Source of the image: Created by processing content from “Reiwa 6 Edition Information and Communications White Paper, Chapter 3, Section 2: Technologies Advancing with AI Evolution” (https://www.soumu.go.jp/johotsusintokei/whitepaper/ja/r06/pdf/n1410000.pdf)：(Ministry of Internal Affairs and Communications). + +## 📣 Release + +- Released YomiToku vX.X.X on December XX, 2024. + +## 💡 Installation + +``` +pip install yomitoku +``` + + +- Please install the version of PyTorch that matches your CUDA version. By default, a version compatible with CUDA 12.4 or higher will be installed. +- PyTorch versions 2.5 and above are supported. As a result, CUDA version 11.8 or higher is required. If this is not feasible, please use the Dockerfile provided in the repository. + +## 🚀 Usage + +``` +yomitoku ${path_data} -f md -o results -v --figure +``` + +- `${path_data}`: Specify the path to a directory containing images to be analyzed or directly provide the path to an image file. If a directory is specified, images in its subdirectories will also be processed. +- `-f`, `--format`: Specify the output file format. Supported formats are json, csv, html, and md. +- `-o`, `--outdir`: Specify the name of the output directory. If it does not exist, it will be created. +- `-v`, `--vis`: If specified, outputs visualized images of the analysis results. +- `-d`, `--device`: Specify the device for running the model. If a GPU is unavailable, inference will be executed on the CPU. (Default: cuda) +- `--ignore_line_break`: Ignores line breaks in the image and concatenates sentences within a paragraph. (Default: respects line breaks as they appear in the image.) +- `--figure_letter`: Exports characters contained within detected figures and tables to the output file. +- `--figure`: Exports detected figures and images to the output file (supported only for html and markdown). + + +For other options, please refer to the help documentation. +``` +yomitoku --help +``` + +**NOTE** +- It is recommended to run on a GPU. The system is not optimized for inference on CPUs, which may result in significantly longer processing times. +- Only printed text recognition is supported. While it may occasionally read handwritten text, official support is not provided. +- YomiToku is optimized for document OCR and is not designed for scene OCR (e.g., text printed on non-paper surfaces like signs). +- The resolution of input images is critical for improving the accuracy of AI-OCR recognition. Low-resolution images may lead to reduced recognition accuracy. It is recommended to use images with a minimum short side resolution of 720px for inference. + +## 📝 Documents + +For more details, please refer to the [documentation](https://kotaro-kinoshita.github.io/yomitoku-dev/) + +## LICENSE + +The source code stored in this repository and the model weight files related to this project on Hugging Face Hub are licensed under CC BY-NC-SA 4.0. +You are free to use them for non-commercial personal use or research purposes. +For commercial use, a separate commercial license is available. Please contact the developers for more information. + +YomiToku © 2024 by MLism Inc. is licensed under CC BY-NC-SA 4.0. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/ diff --git a/docs/index.en.md b/docs/index.en.md new file mode 100644 index 0000000..efa42b6 --- /dev/null +++ b/docs/index.en.md @@ -0,0 +1,21 @@ +## 🌟 Introduction + +YomiToku is a Document AI engine specialized in Japanese document image analysis. It provides full OCR (optical character recognition) and layout analysis capabilities, enabling the recognition, extraction, and conversion of text and diagrams from images. + +- 🤖 Equipped with four AI models trained on Japanese datasets: text detection, text recognition, layout analysis, and table structure recognition. All models are independently trained and optimized for Japanese documents, delivering high-precision inference. +- 🇯🇵 Each model is specifically trained for Japanese document images, supporting the recognition of over 7,000 Japanese characters, including vertical text and other layout structures unique to Japanese documents. (It also supports English documents.) +- 📈 By leveraging layout analysis, table structure parsing, and reading order estimation, it extracts information while preserving the semantic structure of the document layout. +- 📄 Supports a variety of output formats, including HTML, Markdown, JSON, and CSV. It also allows for the extraction of diagrams and images contained within the documents. +- ⚡ Operates efficiently in GPU environments, enabling fast document transcription and analysis. It requires less than 8GB of VRAM, eliminating the need for high-end GPUs.。 + +## 🙋 FAQ + +### Q. Is it possible to use YomiToku in an environment without internet access? +A. Yes, it is possible. +YomiToku connects to Hugging Face Hub to automatically download model files during the first execution, requiring internet access at that time. However, you can manually download the files in advance, allowing YomiToku to operate in an offline environment. For details, please refer to Usage under the section "Using YomiToku in an Offline Environment." + +### Q. Is commercial use allowed? +A. This package is licensed under CC BY-NC 4.0. It is available for free for personal and research purposes. For commercial use, a paid commercial license is required. Please contact the developers for further details. + +### Q. Can handwritten text be recognized? +A. Only printed text recognition is supported. While handwritten text may occasionally be recognized, it is not officially supported. diff --git a/docs/index.md b/docs/index.ja.md similarity index 82% rename from docs/index.md rename to docs/index.ja.md index dfeaffe..d9db45c 100644 --- a/docs/index.md +++ b/docs/index.ja.md @@ -8,20 +8,6 @@ YomiToku は日本語に特化した AI 文章画像解析エンジン(Document - 📄 多様な出力形式をサポートしています。html やマークダウン、json、csv のいずれかのフォーマットに変換可能です。また、文書内に含まれる図表、画像の抽出の出力も可能です。 - ⚡ GPU環境で高速に動作し、効率的に文書の文字起こし解析が可能です。また、VRAMも8GB以内で動作し、ハイエンドなGPUを用意する必要はありません。 -## 🚀 主な機能 - -- 日本語の文書画像向けの汎用 AI-OCR 機能 -- 文書画像のレイアウト解析 AI による図表や段落の自動推定機能 -- 表の構造解析 AI による表の行列構造の認識機能 - -## 🔥 今後の開発予定 - -YomiToku は現在も開発中であり、今後以下のような機能の拡張を目指しています。 - -- 手書き文字の認識 -- 数式の検知や認識、Latex フォーマットへの変換 -- レイアウト解析機能の拡張による識別要素の細分化(タイトル、見出し、図表のキャプション) - ## 🙋 FAQ ### Q. インターネットに接続できない環境での動作は可能ですか？ diff --git a/docs/installation.en.md b/docs/installation.en.md new file mode 100644 index 0000000..cd07e9b --- /dev/null +++ b/docs/installation.en.md @@ -0,0 +1,47 @@ +# Installation + + +This package requires Python 3.9 or later and PyTorch 2.5 or later for execution. PyTorch must be installed according to your CUDA version. A GPU with more than 8GB of VRAM is recommended. While it can run on a CPU, please note that the processing is not currently optimized for CPUs, which may result in longer execution times. + +## from PYPI + +```bash +pip install yomitoku +``` + +## using uv +This repository uses the package management tool [uv](https://docs.astral.sh/uv/). After installing uv, clone the repository and execute the following commands: + +```bash +uv sync +``` + +When using uv, you need to modify the following part of the pyproject.toml file to match your CUDA version. By default, PyTorch compatible with CUDA 12.4 will be downloaded. + +```pyproject.tom +[[tool.uv.index]] +name = "pytorch-cuda124" +url = "https://download.pytorch.org/whl/cu124" +explicit = true +``` + + +## using docker + +A Dockerfile is provided in the root of the repository, which you are welcome to use. + +```bash +docker build -t yomitoku . +``` + +=== "GPU" + + ```bash + docker run -it --gpus all -v $(pwd):/workspace --name yomitoku yomitoku /bin/bash + ``` + +=== "CPU" + + ```bash + docker run -it -v $(pwd):/workspace --name yomitoku yomitoku /bin/bash + ``` diff --git a/docs/installation.md b/docs/installation.ja.md similarity index 65% rename from docs/installation.md rename to docs/installation.ja.md index 9a16b76..5315e58 100644 --- a/docs/installation.md +++ b/docs/installation.ja.md @@ -8,14 +8,23 @@ pip install yomitoku ``` -## UV でのインストール +## uv でのインストール -本リポジトリはパッケージ管理ツールに [UV](https://docs.astral.sh/uv/) を使用しています。UV をインストール後、リポジトリをクローンし、以下のコマンドを実行してください +本リポジトリはパッケージ管理ツールに [uv](https://docs.astral.sh/uv/) を使用しています。uv をインストール後、リポジトリをクローンし、以下のコマンドを実行してください ```bash uv sync ``` +uvを利用する場合、`pyproject.toml`の以下の部分をご自身のcudaのバージョンに合わせて修正する必要があります。デフォルトではCUDA12.4に対応したpytorchがダウンロードされます。 + +```pyproject.tom +[[tool.uv.index]] +name = "pytorch-cuda124" +url = "https://download.pytorch.org/whl/cu124" +explicit = true +``` + ## Docker 環境での実行 リポジトリの直下に dockerfile を配置していますので、そちらも活用いただけます。 diff --git a/docs/usage.en.md b/docs/usage.en.md new file mode 100644 index 0000000..d68b3f6 --- /dev/null +++ b/docs/usage.en.md @@ -0,0 +1,223 @@ +# Usage + +## CLI + +The model weight files are downloaded from Hugging Face Hub only during the first execution. + +``` +yomitoku ${path_data} -f md -o results -v +``` + +- `${path_data}`: Specify the path to a directory containing images to be analyzed or directly provide the path to an image file. If a directory is specified, images in its subdirectories will also be processed. +- `-f`, `--format`: Specify the output file format. Supported formats are json, csv, html, and md. +- `-o`, `--outdir`: Specify the name of the output directory. If it does not exist, it will be created. +- `-v`, `--vis`: If specified, outputs visualized images of the analysis results. +- `-d`, `--device`: Specify the device for running the model. If a GPU is unavailable, inference will be executed on the CPU. (Default: cuda) +- `--ignore_line_break`: Ignores line breaks in the image and concatenates sentences within a paragraph. (Default: respects line breaks as they appear in the image.) +- `--figure_letter`: Exports characters contained within detected figures and tables to the output file. +- `--figure`: Exports detected figures and images to the output file (supported only for html and markdown). + +**NOTE** +- It is recommended to run on a GPU. The system is not optimized for inference on CPUs, which may result in significantly longer processing times. +- Only printed text recognition is supported. While it may occasionally read handwritten text, official support is not provided. +- YomiToku is optimized for document OCR and is not designed for scene OCR (e.g., text printed on non-paper surfaces like signs). +- The resolution of input images is critical for improving the accuracy of AI-OCR recognition. Low-resolution images may lead to reduced recognition accuracy. It is recommended to use images with a minimum short side resolution of 720px for inference. + +## Calling from within Python code + +### Document Analyzer の利用 + +The Document Analyzer performs OCR and layout analysis, integrating these results into a comprehensive analysis output. It can be used for various use cases, including paragraph and table structure analysis, extraction, and figure/table detection. + +```python +import cv2 + +from yomitoku import DocumentAnalyzer +from yomitoku.data.functions import load_image + +if __name__ == "__main__": + img = load_image(PATH_IMAGE) + analyzer = DocumentAnalyzer(configs=None, visualize=True, device="cuda") + results, ocr_vis, layout_vis = analyzer(img) + + # HTML形式で解析結果をエクスポート + results.to_html(PATH_OUTPUT) + + # 可視化画像を保存 + cv2.imwrite("output_ocr.jpg", ocr_vis) + cv2.imwrite("output_layout.jpg", layout_vis) +``` + +- Setting `visualize` to True enables the visualization of each processing result. The second and third return values will contain the OCR and layout analysis results, respectively. If set to False, None will be returned. Since visualization adds computational overhead, it is recommended to set it to False unless needed for debugging purposes. +- The `device` parameter specifies the computation device to be used. The default is "cuda". If a GPU is unavailable, it automatically switches to CPU mode for processing. +- The `configs` parameter allows you to set more detailed parameters for the pipeline processing. + +The results of DocumentAnalyzer can be exported in the following formats: + +`to_json()`: JSON format (*.json) +`to_html()`: HTML format (*.html) +`to_csv()`: Comma-separated CSV format (*.csv) +`to_markdown()`: Markdown format (*.md) + + +### Using AI-OCR Only + +AI-OCR performs text detection and recognition on the detected text, returning the positions of the text within the image along with the recognition results. + +```python +import cv2 + +from yomitoku import OCR +from yomitoku.data.functions import load_image + +if __name__ == "__main__": + img = load_image(PATH_IMAGE) + ocr = OCR(configs=None, visualize=True, device="cuda") + results, ocr_vis = ocr(img) + + # JSON形式で解析結果をエクスポート + results.to_json(PATH_OUTPUT) + cv2.imwrite("output_ocr.jpg", ocr_vis) +``` + +- Setting `visualize` to True enables the visualization of each processing result. The second and third return values will contain the OCR and layout analysis results, respectively. If set to False, None will be returned. Since visualization adds computational overhead, it is recommended to set it to False unless needed for debugging purposes. +- The `device` parameter specifies the computation device to be used. The default is "cuda". If a GPU is unavailable, it automatically switches to CPU mode for processing. +- The `configs` parameter allows you to set more detailed parameters for the pipeline processing. + +The results of OCR processing support export in JSON format (`to_json()`) only. + +### Using Layout Analyzer only + +The `LayoutAnalyzer` performs text detection, followed by AI-based paragraph, figure/table detection, and table structure analysis. It analyzes the layout structure within the document. + +```python +import cv2 + +from yomitoku import LayoutAnalyzer +from yomitoku.data.functions import load_image + +if __name__ == "__main__": + img = load_image(PATH_IMAGE) + analyzer = LayoutAnalyzer(configs=None, visualize=True, device="cuda") + results, layout_vis = analyzer(img) + + # JSON形式で解析結果をエクスポート + results.to_json(PATH_OUTPUT) + cv2.imwrite("output_layout.jpg", layout_vis) +``` + + +- Setting `visualize` to True enables the visualization of each processing result. The second and third return values will contain the OCR and layout analysis results, respectively. If set to False, None will be returned. Since visualization adds computational overhead, it is recommended to set it to False unless needed for debugging purposes. +- The `device` parameter specifies the computation device to be used. The default is "cuda". If a GPU is unavailable, it automatically switches to CPU mode for processing. +- The `configs` parameter allows you to set more detailed parameters for the pipeline processing. + +The results of LayoutAnalyzer processing support export only in JSON format (to_json()). + +## Detailed Configuration of the Pipeline + +By providing a config, you can adjust the behavior in greater detail. + +### How to Write a Config + +The config is provided in dictionary format. By using a config, you can execute processing on different devices for each module and set detailed parameters. For example, the following config allows the OCR processing to run on a GPU, while the layout analysis is performed on a CPU: + +```python +from yomitoku import DocumentAnalyzer + +if __name__ == "__main__": + configs = { + "ocr": { + "text_detector": { + "device": "cuda", + }, + "text_recognizer": { + "device": "cuda", + }, + }, + "layout_analyzer": { + "layout_parser": { + "device": "cpu", + }, + "table_structure_recognizer": { + "device": "cpu", + }, + }, + } + + DocumentAnalyzer(configs=configs) +``` + +### Defining Parameters in an YAML File + + +By providing the path to a YAML file in the config, you can adjust detailed parameters for inference. Examples of YAML files can be found in the `configs` directory within the repository. While the model's network parameters cannot be modified, certain aspects like post-processing parameters and input image size can be adjusted. + +For instance, you can define post-processing thresholds for the Text Detector in a YAML file and set its path in the config. The config file does not need to include all parameters; you only need to specify the parameters that require changes. + +```text_detector.yaml +post_process: + thresh: 0.1 + unclip_ratio: 2.5 +``` + +Storing the Path to a YAML File in the Config + +```python +from yomitoku import DocumentAnalyzer + +if __name__ == "__main__": + # path_cfgに設定したymalのパスを記述する + configs = { + "ocr": { + "text_detector": { + "path_cfg": "text_detector.yaml" + } + } + } + + DocumentAnalyzer(configs=configs) +``` + +## Using in an Offline Environment + +Yomitoku automatically downloads models from Hugging Face Hub during the first execution, requiring an internet connection at that time. However, by manually downloading the models in advance, it can be executed in an offline environment. + +1. Install [Git Large File Storage](https://docs.github.com/ja/repositories/working-with-files/managing-large-files/installing-git-large-file-storage) +2. In an environment with internet access, download the model repository. Copy the cloned repository to your target environment using your preferred tools. + + +The following is the command to download the model repository from Hugging Face Hub. + +```sh +git clone https://huggingface.co/KotaroKinoshita/yomitoku-table-structure-recognizer-rtdtrv2-open-beta + +git clone https://huggingface.co/KotaroKinoshita/yomitoku-layout-parser-rtdtrv2-open-beta + +git clone https://huggingface.co/KotaroKinoshita/yomitoku-text-detector-dbnet-open-beta + +git clone https://huggingface.co/KotaroKinoshita/yomitoku-text-recognizer-parseq-open-beta +``` + +3. Place the model repository directly under the root directory of the Yomitoku repository and reference the local model repository in the `hf_hub_repo` field of the YAML file. Below is an example of `text_detector.yaml`. Similarly, define YAML files for other modules as well. + +```yaml +hf_hub_repo: yomitoku-text-detector-dbnet-open-beta +``` + +4. Storing the Path to a YAML File in the Config + +```python +from yomitoku import DocumentAnalyzer + +if __name__ == "__main__": + # path_cfgに設定したymalのパスを記述する + configs = { + "ocr": { + "text_detector": { + "path_cfg": "text_detector.yaml" + } + } + } + + DocumentAnalyzer(configs=configs) +``` diff --git a/docs/usage.md b/docs/usage.ja.md similarity index 100% rename from docs/usage.md rename to docs/usage.ja.md diff --git a/mkdocs.yml b/mkdocs.yml index 86e761d..1ae2e68 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -11,7 +11,7 @@ theme: palette: - media: "(prefers-color-scheme: light)" scheme: default - primary: brown + primary: indigo accent: blue toggle: icon: material/toggle-switch @@ -70,3 +70,15 @@ nav: - Usage: usage.md repo_url: https://github.com/kotaro-kinoshita/yomitoku-dev + +plugins: + - search: + - i18n: + default_language: ja + languages: + - locale: ja + name: 日本語 + default: true + - locale: en + name: English + link: /en/ diff --git a/pyproject.toml b/pyproject.toml index ad2d6ca..14b8afd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -9,7 +9,7 @@ source = "uv-dynamic-versioning" name = "yomitoku" dynamic = ["version"] authors = [{name = "Kotaro Kinoshita", email = "kotaro.kinoshita@mlism.com"}] -description = "Yomitoku is a document image analysis package powered by AI technology for the Japanese language." +description = "Yomitoku is an AI-powered document image analysis package designed specifically for the Japanese language." readme = "README.md" license = {text = "CC BY-NC-SA 4.0"} requires-python = ">=3.9" @@ -24,7 +24,7 @@ dependencies = [ "shapely>=2.0.6", "timm>=1.0.11", "torchvision>=0.20.0", - 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{ name = "torch", marker = "platform_system != 'Darwin' and platform_system != 'Linux' and platform_system != 'Windows'", specifier = ">=2.5.0" }, - { name = "torch", marker = "platform_system == 'Darwin'", specifier = ">=2.5.0", index = "https://download.pytorch.org/whl/cpu" }, - { name = "torch", marker = "platform_system == 'Linux'", specifier = ">=2.5.0", index = "https://download.pytorch.org/whl/cu124" }, - { name = "torch", marker = "platform_system == 'Windows'", specifier = ">=2.5.0", index = "https://download.pytorch.org/whl/cu124" }, - { name = "torchvision", marker = "platform_system != 'Darwin' and platform_system != 'Linux' and platform_system != 'Windows'", specifier = ">=0.20.0" }, + { name = "torch", marker = "platform_system != 'Darwin' and platform_system != 'Windows'", specifier = "==2.5.0" }, + { name = "torch", marker = "platform_system == 'Darwin'", specifier = "==2.5.0", index = "https://download.pytorch.org/whl/cpu" }, + { name = "torch", marker = "platform_system == 'Windows'", specifier = "==2.5.0", index = "https://download.pytorch.org/whl/cu124" }, + { name = "torchvision", marker = "platform_system != 'Darwin' and platform_system != 'Windows'", specifier = ">=0.20.0" }, { name = "torchvision", marker = "platform_system == 'Darwin'", specifier = ">=0.20.0", index = "https://download.pytorch.org/whl/cpu" }, - { name = "torchvision", marker = "platform_system == 'Linux'", specifier = ">=0.20.0", index = "https://download.pytorch.org/whl/cu124" }, { name = "torchvision", marker = "platform_system == 'Windows'", specifier = ">=0.20.0", index = "https://download.pytorch.org/whl/cu124" }, ] @@ -1869,6 +1765,9 @@ requires-dist = [ dev = [ { name = "mkdocs", specifier = ">=1.6.1" }, { name = "mkdocs-material", specifier = ">=9.5.44" }, + { name = "mkdocs-static-i18n" }, + { name = "plantuml-markdown" }, + { name = "pygments" }, { name = "pytest", specifier = ">=8.3.3" }, { name = "pytest-cov", specifier = ">=5.0.0" }, { name = "ruff", specifier = ">=0.7.0" }, From 67cec05dcd865d6b6688284abdca22f0b88ebd06 Mon Sep 17 00:00:00 2001 From: kotaro-kinoshita Date: Tue, 26 Nov 2024 16:25:40 +0900 Subject: [PATCH 2/3] add english document --- README.md | 2 +- README_EN.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 18c88c2..52dd41e 100644 --- a/README.md +++ b/README.md @@ -88,4 +88,4 @@ yomitoku --help 非商用での個人利用、研究目的での利用はご自由にお使いください。 商用目的での利用に関しては、別途、商用ライセンスを提供しますので、開発者にお問い合わせください。 -YomiToku © 2024 by MLism Inc. is licensed under CC BY-NC-SA 4.0. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/ +YomiToku © 2024 by Kotaro Kinoshita is licensed under CC BY-NC-SA 4.0. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/ diff --git a/README_EN.md b/README_EN.md index 3e90a6e..87084e6 100644 --- a/README_EN.md +++ b/README_EN.md @@ -90,4 +90,4 @@ The source code stored in this repository and the model weight files related to You are free to use them for non-commercial personal use or research purposes. For commercial use, a separate commercial license is available. Please contact the developers for more information. -YomiToku © 2024 by MLism Inc. is licensed under CC BY-NC-SA 4.0. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/ +YomiToku © 2024 by Kotaro Kinoshita is licensed under CC BY-NC-SA 4.0. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/ From c2ad6fefc3a52e29598c4167c81d12058e6fb138 Mon Sep 17 00:00:00 2001 From: kotaro-kinoshita Date: Tue, 26 Nov 2024 16:31:31 +0900 Subject: [PATCH 3/3] add english document --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 52dd41e..eae3231 100644 --- a/README.md +++ b/README.md @@ -40,7 +40,7 @@ Markdown でエクスポートした結果は関してはリポジトリ内の[s ## 📣 リリース情報 -- 2024 年 12 月 XX YomiToku vX.X.X を公開 +- 2024 年 11 月 26 YomiToku v0.5.0 ベータ版を公開 ## 💡 インストールの方法