diff --git a/docs/docs/configuration/license_plate_recognition.md b/docs/docs/configuration/license_plate_recognition.md
index 7babcaf4d..36e8b7dad 100644
--- a/docs/docs/configuration/license_plate_recognition.md
+++ b/docs/docs/configuration/license_plate_recognition.md
@@ -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
 
 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.