A real-time desktop GUI for DeepSeek-OCR, this Electron-based application provides a user-friendly interface for optical character recognition tasks. It targets users who require a local, efficient solution for extracting text from images, benefiting from GPU acceleration for faster processing and offering convenient features like drag-and-drop uploads and direct result copying.

How It Works

The client utilizes Electron to build a desktop application that interfaces with the DeepSeek-OCR model. It processes uploaded images, leveraging CUDA-enabled NVIDIA GPUs for accelerated OCR inference. The application supports drag-and-drop image uploads and allows users to click on recognized text regions to copy them directly, with results exportable in a ZIP archive format.

Quick Start & Requirements

• Primary install / run command: On Windows, extract the provided ZIP file and run start-client.bat. The first execution automatically installs necessary Node.js and Python dependencies.
• Non-default prerequisites and dependencies: Windows 10/11 (other OS are experimental), Node.js 18+, Python 3.12+, and an NVIDIA GPU with CUDA support are required.
• Estimated setup time or resource footprint: Initial dependency installation may take time.
• Links: Links to Node.js and Python downloads are mentioned but not directly provided.

Highlighted Details

• Real-time OCR processing with drag-and-drop image upload functionality.
• GPU acceleration via CUDA for enhanced processing speed.
• Export OCR results as a ZIP archive, including markdown images.
• Clickable regions within the OCR output for easy text copying.

Maintenance & Community

The project is described as "quickly put together" with "Code cleanup needed," suggesting an early-stage development status. Contributions via Pull Requests (PRs) are actively encouraged, especially for improving Linux/macOS support and addressing known issues. Future goals include TypeScript conversion, an auto-updater, PDF support, batch processing, and potential CPU support.

Licensing & Compatibility

The project is released under the MIT license, which permits unrestricted use, modification, and distribution, including for commercial applications.

Limitations & Caveats

Support for Linux and macOS is experimental and untested, requiring manual execution via start-client.sh and user-submitted fixes. A known issue affects image processing at default resolutions, necessitating app restarts. CPU support is a future goal, implying it is not currently available. The codebase requires cleanup, indicating it may not be production-ready.