A local-first, ML-powered desktop application for translating manga, built in Rust with automated text detection, OCR, inpainting, and LLM translation.
Koharu is an open-source desktop application that automates manga translation using machine learning. It combines object detection, OCR, inpainting, and LLM-based translation into a single local-first workflow, allowing users to translate manga pages while keeping their data private. The application is built in Rust for performance and safety.
Manga translators, scanlation groups, and enthusiasts who need an efficient, privacy-focused tool for translating manga without relying on cloud services. It's also suitable for developers interested in ML applications for creative workflows.
Developers choose Koharu for its fully local execution, which ensures data privacy, and its comprehensive pipeline that handles the entire translation process automatically. Its use of Rust and modern ML frameworks provides high performance and reliability compared to manual or cloud-dependent alternatives.
ML-powered manga translator, written in Rust.
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All vision and language models run locally on the user's machine, ensuring data never leaves the device, as emphasized in the philosophy and note sections.
Combines text detection, OCR, inpainting, and LLM translation into a single pipeline, streamlining the entire manga translation process as described in the key features.
Supports vertical CJK layout, RTL scripts, and precise font fallback, making translated text fit naturally into speech bubbles, detailed in the text rendering section.
Exports pages as layered Photoshop files with editable text layers, facilitating manual refinement, which is highlighted in the export and features sections.
Requires powerful GPUs and significant memory for local model inference, with dependencies on CUDA, Metal, or Vulkan, limiting accessibility on older systems as noted in the GPU acceleration section.
Involves downloading large models, configuring GPU drivers, and managing dependencies like CUDA Toolkit or AMD HIP SDK, making initial installation cumbersome, as outlined in the installation prerequisites.
Lacks built-in cloud sync or team collaboration tools, focusing on local-first workflows, which may hinder use in group scanlation projects despite the API for automation.