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Update docs/docs/configuration/license_plate_recognition.md

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Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>
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@ -28,14 +28,6 @@ In the default mode, Frigate's LPR needs to first detect a `car` or `motorcycle`
::: :::
## OCR Model Details
Frigate uses **PaddleOCR** (via an ONNX runtime) to perform text recognition on detected license plates. This model extracts characters from license plates and populates the `recognized_license_plate` field or a `sub_label` for tracked `car` or `motorcycle` objects. PaddleOCR provides accurate recognition even in challenging lighting and motion conditions, and it is lightweight enough to run on CPU or GPU depending on your configuration.
- The PaddleOCR ONNX model is used internally and can be replaced with custom OCR models if desired.
- For more details, visit the [PaddleOCR GitHub repository](https://github.com/PaddlePaddle/PaddleOCR).
## Minimum System Requirements ## Minimum System Requirements
License plate recognition works by running AI models locally on your system. The YOLOv9 plate detector model and the OCR models ([PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)) are relatively lightweight and can run on your CPU or GPU, depending on your configuration. At least 4GB of RAM is required. License plate recognition works by running AI models locally on your system. The YOLOv9 plate detector model and the OCR models ([PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)) are relatively lightweight and can run on your CPU or GPU, depending on your configuration. At least 4GB of RAM is required.