andyhuo520/ppocrv6-studio

PP-OCRv6 Studio is a local text-recognition tool that runs entirely on your own machine. You drag in images, scanned documents, photos of business cards, even pictures of tire sidewalls or elevator LED displays, and it extracts the text for you. Nothing is sent to the cloud, and you can switch between three model sizes (Tiny, Small, Medium) with one click depending on whether you want speed or maximum accuracy. Behind the scenes, the tool splits the work into two steps: first it finds where the text is in your image, then it reads the characters in each region. It uses PP-OCRv6, a model family from PaddlePaddle, packaged in a format called ONNX so it can run without the original training framework. On Apple Silicon Macs it automatically taps into CoreML hardware acceleration. There is also a zero-dependency HTML file that runs the smallest model entirely in your browser with no server needed at all. The target audience is anyone who needs reliable text extraction from difficult, real-world images, not just clean PDFs. The project highlights tough cases like dot-matrix fonts, curved embossed metal, and reflective seven-segment displays. A founder digitizing stacks of physical business cards, a PM extracting serial numbers from photos of installed equipment, or a researcher benchmarking different OCR engines against a standard test set would all find this useful. Results can be exported as CSV, Markdown, or Excel for downstream workflows. What stands out is the transparency of the benchmarking. The repository includes scripts to run a standard 18-image evaluation set and compare results against macOS's built-in Apple Vision framework. The included real-world test panels show exactly what the model detected and read, making it easy to judge quality visually rather than trusting an abstract accuracy number. The tradeoff is that it is primarily built and tuned for Apple Silicon Macs, though it can fall back to CPU mode on Intel Macs or Linux with some manual configuration.