The 47ms Pipeline That Actually Works
Most ALPR (Automatic License Plate Recognition) tutorials stop at "detect the plate, then run OCR." What they don't tell you: the handoff between YOLOv8 and PaddleOCR is where everything falls apart. Mismatched color spaces, coordinate transforms that drift by 2-3 pixels, preprocessing pipelines fighting each other — I've burned through all of these.
Here's what actually works: a two-stage pipeline that runs at 21 FPS on a laptop GPU, with 94% read accuracy on real-world footage (tested on 500 dashcam frames from varying angles, lighting, and motion blur).
Why Two Models Beat One
You might think: why not train YOLOv8 to predict characters directly? I tried. The problem is scale variance.
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