chrisipanaque/baidu-unlimited-ocr-local-server

This project lets you run Baidu's Unlimited-OCR, a large document reading AI model, on a regular consumer gaming graphics card instead of a cloud service or expensive datacenter hardware. OCR means optical character recognition, turning a photo of text into actual readable text. Unlimited-OCR is described as a 3 billion parameter model that combines several vision and language components to read whole pages of documents in one pass, and it scores well on a standard document parsing benchmark. The practical problem this project solves is memory. The full model plus its supporting vision components need more graphics memory than an 8 gigabyte card like the RTX 4060 normally has room for. The project works around this by moving part of the vision processing onto the computer's regular memory and processor instead of the graphics card, using a lighter image resolution mode, and limiting how much text the model can generate at once. Together these changes keep memory use under the card's limit. Setting it up involves cloning the repository, creating a Python virtual environment, installing a specific version of PyTorch matched to a particular CUDA driver version, providing a HuggingFace access token so the roughly 13 gigabyte model can download, and then starting a small web server. Once running, you open a webpage in your browser that captures a photo from your webcam, sends it to the server, and streams back the recognized text as it's generated. There are also optional instructions for exposing the local server to the internet temporarily using tunneling tools. The README includes a detailed hardware requirements table, a breakdown of exactly how graphics memory is spent, and a troubleshooting section covering common errors like running out of graphics memory or installing the wrong PyTorch build. It credits the original Baidu model, a related open-source OCR project, and a public demo that inspired its streaming approach.