License Plate Recognition (LPR)

Frigate can recognize license plates on vehicles and automatically add the detected characters to the recognized_license_plate field or a known name as a sub_label to tracked objects of type car or motorcycle. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.

LPR works best when the license plate is clearly visible to the camera. For moving vehicles, Frigate continuously refines the recognition process, keeping the most confident result. When a vehicle becomes stationary, LPR continues to run for a short time after to attempt recognition.

When a plate is recognized, the details are:

Added as a sub_label (if known) or the recognized_license_plate field (if unknown) to a tracked object.
Viewable in the Details pane in Review/History.
Viewable in the Tracked Object Details pane in Explore (sub labels and recognized license plates).
Filterable through the More Filters menu in Explore.
Published via the frigate/events MQTT topic as a sub_label (known) or recognized_license_plate (unknown) for the car or motorcycle tracked object.
Published via the frigate/tracked_object_update MQTT topic with name (if known) and plate.

Model Requirements

Users running a Frigate+ model (or any custom model that natively detects license plates) should ensure that license_plate is added to the list of objects to track either globally or for a specific camera. This will improve the accuracy and performance of the LPR model.

Users without a model that detects license plates can still run LPR. Frigate uses a lightweight YOLOv9 license plate detection model that can be configured to run on your CPU or GPU. In this case, you should not define license_plate in your list of objects to track.

note

In the default mode, Frigate's LPR needs to first detect a car or motorcycle before it can recognize a license plate. If you're using a dedicated LPR camera and have a zoomed-in view where a car or motorcycle will not be detected, you can still run LPR, but the configuration parameters will differ from the default mode. See the Dedicated LPR Cameras section below.

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) are relatively lightweight and can run on your CPU or GPU, depending on your configuration. At least 4GB of RAM and a CPU with AVX + AVX2 instructions is required.

Configuration

License plate recognition is disabled by default. Enable it in your config file:

lpr:
  enabled: True

Like other enrichments in Frigate, LPR must be enabled globally to use the feature. You should disable it for specific cameras at the camera level if you don't want to run LPR on cars on those cameras:

cameras:
  garage:
    ...
    lpr:
      enabled: False

For non-dedicated LPR cameras, ensure that your camera is configured to detect objects of type car or motorcycle, and that a car or motorcycle is actually being detected by Frigate. Otherwise, LPR will not run.

Like the other real-time processors in Frigate, license plate recognition runs on the camera stream defined by the detect role in your config. To ensure optimal performance, select a suitable resolution for this stream in your camera's firmware that fits your specific scene and requirements.

Advanced Configuration

Fine-tune the LPR feature using these optional parameters at the global level of your config. The only optional parameters that can be set at the camera level are enabled, min_area, and enhancement.

Detection

detection_threshold: License plate object detection confidence score required before recognition runs.
- Default: 0.7
- Note: This is field only applies to the standalone license plate detection model, threshold and min_score object filters should be used for models like Frigate+ that have license plate detection built in.
min_area: Defines the minimum area (in pixels) a license plate must be before recognition runs.
- Default: 1000 pixels. Note: this is intentionally set very low as it is an area measurement (length x width). For reference, 1000 pixels represents a ~32x32 pixel square in your camera image.
- Depending on the resolution of your camera's detect stream, you can increase this value to ignore small or distant plates.
device: Device to use to run license plate detection and recognition models.
- Default: None
- This is auto-selected by Frigate and can be CPU, GPU, or the GPU's device number. For users without a model that detects license plates natively, using a GPU may increase performance of the YOLOv9 license plate detector model. See the Hardware Accelerated Enrichments documentation. However, for users who run a model that detects license_plate natively, there is little to no performance gain reported with running LPR on GPU compared to the CPU.
model_size: The size of the model used to identify regions of text on plates.
- Default: small
- This can be small or large.
- The small model is fast and identifies groups of Latin and Chinese characters.
- The large model identifies Latin characters only, and uses an enhanced text detector to find characters on multi-line plates. It is significantly slower than the small model.
- If your country or region does not use multi-line plates, you should use the small model as performance is much better for single-line plates.

Recognition

recognition_threshold: Recognition confidence score required to add the plate to the object as a recognized_license_plate and/or sub_label.
- Default: 0.9.
min_plate_length: Specifies the minimum number of characters a detected license plate must have to be added as a recognized_license_plate and/or sub_label to an object.
- Use this to filter out short, incomplete, or incorrect detections.
format: A regular expression defining the expected format of detected plates. Plates that do not match this format will be discarded.
- "^[A-Z]{1,3} [A-Z]{1,2} [0-9]{1,4}$" matches plates like "B AB 1234" or "M X 7"
- "^[A-Z]{2}[0-9]{2} [A-Z]{3}$" matches plates like "AB12 XYZ" or "XY68 ABC"
- Websites like https://regex101.com/ can help test regular expressions for your plates.

Matching

known_plates: List of strings or regular expressions that assign custom a sub_label to car and motorcycle objects when a recognized plate matches a known value.
- These labels appear in the UI, filters, and notifications.
- Unknown plates are still saved but are added to the recognized_license_plate field rather than the sub_label.
match_distance: Allows for minor variations (missing/incorrect characters) when matching a detected plate to a known plate.
- For example, setting match_distance: 1 allows a plate ABCDE to match ABCBE or ABCD.
- This parameter will not operate on known plates that are defined as regular expressions. You should define the full string of your plate in known_plates in order to use match_distance.

Image Enhancement

enhancement: A value between 0 and 10 that adjusts the level of image enhancement applied to captured license plates before they are processed for recognition. This preprocessing step can sometimes improve accuracy but may also have the opposite effect.
- Default: 0 (no enhancement)
- Higher values increase contrast, sharpen details, and reduce noise, but excessive enhancement can blur or distort characters, actually making them much harder for Frigate to recognize.
- This setting is best adjusted at the camera level if running LPR on multiple cameras.
- If Frigate is already recognizing plates correctly, leave this setting at the default of 0. However, if you're experiencing frequent character issues or incomplete plates and you can already easily read the plates yourself, try increasing the value gradually, starting at 5 and adjusting as needed. You should see how different enhancement levels affect your plates. Use the debug_save_plates configuration option (see below).

Normalization Rules

replace_rules: List of regex replacement rules to normalize detected plates. These rules are applied sequentially and are applied before the format regex, if specified. Each rule must have a pattern (which can be a string or a regex) and replacement (a string, which also supports backrefs like \1). These rules are useful for dealing with common OCR issues like noise characters, separators, or confusions (e.g., 'O'→'0').

These rules must be defined at the global level of your lpr config.

lpr:
  replace_rules:
    - pattern: "[%#*?]" # Remove noise symbols
      replacement: ""
    - pattern: "[= ]" # Normalize = or space to dash
      replacement: "-"
    - pattern: "O" # Swap 'O' to '0' (common OCR error)
      replacement: "0"
    - pattern: "I" # Swap 'I' to '1'
      replacement: "1"
    - pattern: '(\w{3})(\w{3})' # Split 6 chars into groups (e.g., ABC123 → ABC-123) - use single quotes to preserve backslashes
      replacement: '\1-\2'

Rules fire in order: In the example above: clean noise first, then separators, then swaps, then splits.
Backrefs (\1, \2) allow dynamic replacements (e.g., capture groups).
Any changes made by the rules are printed to the LPR debug log.
Tip: You can test patterns with tools like regex101.com.

Debugging

debug_save_plates: Set to True to save captured text on plates for debugging. These images are stored in /media/frigate/clips/lpr, organized into subdirectories by <camera>/<event_id>, and named based on the capture timestamp.
- These saved images are not full plates but rather the specific areas of text detected on the plates. It is normal for the text detection model to sometimes find multiple areas of text on the plate. Use them to analyze what text Frigate recognized and how image enhancement affects detection.
- Note: Frigate does not automatically delete these debug images. Once LPR is functioning correctly, you should disable this option and manually remove the saved files to free up storage.

Configuration Examples

These configuration parameters are available at the global level of your config. The only optional parameters that should be set at the camera level are enabled, min_area, and enhancement.

lpr:
  enabled: True
  min_area: 1500 # Ignore plates with an area (length x width) smaller than 1500 pixels
  min_plate_length: 4 # Only recognize plates with 4 or more characters
  known_plates:
    Wife's Car:
      - "ABC-1234"
      - "ABC-I234" # Accounts for potential confusion between the number one (1) and capital letter I
    Johnny:
      - "J*N-*234" # Matches JHN-1234 and JMN-I234, but also note that "*" matches any number of characters
    Sally:
      - "[S5]LL 1234" # Matches both SLL 1234 and 5LL 1234
    Work Trucks:
      - "EMP-[0-9]{3}[A-Z]" # Matches plates like EMP-123A, EMP-456Z

lpr:
  enabled: True
  min_area: 4000 # Run recognition on larger plates only (4000 pixels represents a 63x63 pixel square in your image)
  recognition_threshold: 0.85
  format: "^[A-Z]{2} [A-Z][0-9]{4}$" # Only recognize plates that are two letters, followed by a space, followed by a single letter and 4 numbers
  match_distance: 1 # Allow one character variation in plate matching
  replace_rules:
    - pattern: "O"
      replacement: "0" # Replace the letter O with the number 0 in every plate
  known_plates:
    Delivery Van:
      - "RJ K5678"
      - "UP A1234"
    Supervisor:
      - "MN D3163"

note

If a camera is configured to detect car or motorcycle but you don't want Frigate to run LPR for that camera, disable LPR at the camera level:

cameras:
  side_yard:
    lpr:
      enabled: False
    ...

Dedicated LPR Cameras

Dedicated LPR cameras are single-purpose cameras with powerful optical zoom to capture license plates on distant vehicles, often with fine-tuned settings to capture plates at night.

To mark a camera as a dedicated LPR camera, add type: "lpr" the camera configuration.

note

Frigate's dedicated LPR mode is optimized for cameras with a narrow field of view, specifically positioned and zoomed to capture license plates exclusively. If your camera provides a general overview of a scene rather than a tightly focused view, this mode is not recommended.

Users can configure Frigate's dedicated LPR mode in two different ways depending on whether a Frigate+ (or native license_plate detecting) model is used:

Using a Frigate+ (or Native `license_plate` Detecting) Model

Users running a Frigate+ model (or any model that natively detects license_plate) can take advantage of license_plate detection. This allows license plates to be treated as standard objects in dedicated LPR mode, meaning that alerts, detections, snapshots, and other Frigate features work as usual, and plates are detected efficiently through your configured object detector.

An example configuration for a dedicated LPR camera using a license_plate-detecting model:

# LPR global configuration
lpr:
  enabled: True
  device: CPU # can also be GPU if available

# Dedicated LPR camera configuration
cameras:
  dedicated_lpr_camera:
    type: "lpr" # required to use dedicated LPR camera mode
    ffmpeg: ... # add your streams
    detect:
      enabled: True
      fps: 5 # increase to 10 if vehicles move quickly across your frame. Higher than 10 is unnecessary and is not recommended.
      min_initialized: 2
      width: 1920
      height: 1080
    objects:
      track:
        - license_plate
      filters:
        license_plate:
          threshold: 0.7
    motion:
      threshold: 30
      contour_area: 60 # use an increased value to tune out small motion changes
      improve_contrast: false
      mask: 0.704,0.007,0.709,0.052,0.989,0.055,0.993,0.001 # ensure your camera's timestamp is masked
    record:
      enabled: True # disable recording if you only want snapshots
    snapshots:
      enabled: True
    review:
      detections:
        labels:
          - license_plate

With this setup:

License plates are treated as normal objects in Frigate.
Scores, alerts, detections, and snapshots work as expected.
Snapshots will have license plate bounding boxes on them.
The frigate/events MQTT topic will publish tracked object updates.
Debug view will display license_plate bounding boxes.
If you are using a Frigate+ model and want to submit images from your dedicated LPR camera for model training and fine-tuning, annotate both the car / motorcycle and the license_plate in the snapshots on the Frigate+ website, even if the car is barely visible.

Using the Secondary LPR Pipeline (Without Frigate+)

If you are not running a Frigate+ model, you can use Frigate’s built-in secondary dedicated LPR pipeline. In this mode, Frigate bypasses the standard object detection pipeline and runs a local license plate detector model on the full frame whenever motion activity occurs.

An example configuration for a dedicated LPR camera using the secondary pipeline:

# LPR global configuration
lpr:
  enabled: True
  device: CPU # can also be GPU if available and correct Docker image is used
  detection_threshold: 0.7 # change if necessary

# Dedicated LPR camera configuration
cameras:
  dedicated_lpr_camera:
    type: "lpr" # required to use dedicated LPR camera mode
    lpr:
      enabled: True
      enhancement: 3 # optional, enhance the image before trying to recognize characters
    ffmpeg: ... # add your streams
    detect:
      enabled: False # disable Frigate's standard object detection pipeline
      fps: 5 # increase if necessary, though high values may slow down Frigate's enrichments pipeline and use considerable CPU
      width: 1920
      height: 1080
    objects:
      track: [] # required when not using a Frigate+ model for dedicated LPR mode
    motion:
      threshold: 30
      contour_area: 60 # use an increased value here to tune out small motion changes
      improve_contrast: false
      mask: 0.704,0.007,0.709,0.052,0.989,0.055,0.993,0.001 # ensure your camera's timestamp is masked
    record:
      enabled: True # disable recording if you only want snapshots
    review:
      detections:
        enabled: True
        retain:
          default: 7

With this setup:

The standard object detection pipeline is bypassed. Any detected license plates on dedicated LPR cameras are treated similarly to manual events in Frigate. You must not specify license_plate as an object to track.
The license plate detector runs on the full frame whenever motion is detected and processes frames according to your detect fps setting.
Review items will always be classified as a detection.
Snapshots will always be saved.
Zones and object masks are not used.
The frigate/events MQTT topic will not publish tracked object updates with the license plate bounding box and score, though frigate/reviews will publish if recordings are enabled. If a plate is recognized as a known plate, publishing will occur with an updated sub_label field. If characters are recognized, publishing will occur with an updated recognized_license_plate field.
License plate snapshots are saved at the highest-scoring moment and appear in Explore.
Debug view will not show license_plate bounding boxes.

Summary

Feature	Native `license_plate` detecting Model (like Frigate+)	Secondary Pipeline (without native model or Frigate+)
License Plate Detection	Uses `license_plate` as a tracked object	Runs a dedicated LPR pipeline
FPS Setting	5 (increase for fast-moving cars)	5 (increase for fast-moving cars, but it may use much more CPU)
Object Detection	Standard Frigate+ detection applies	Bypasses standard object detection
Debug View	May show `license_plate` bounding boxes	May not show `license_plate` bounding boxes
MQTT `frigate/events`	Publishes tracked object updates	Publishes limited updates
Explore	Recognized plates available in More Filters	Recognized plates available in More Filters

By selecting the appropriate configuration, users can optimize their dedicated LPR cameras based on whether they are using a Frigate+ model or the secondary LPR pipeline.

Best practices for using Dedicated LPR camera mode

Tune your motion detection and increase the contour_area until you see only larger motion boxes being created as cars pass through the frame (likely somewhere between 50-90 for a 1920x1080 detect stream). Increasing the contour_area filters out small areas of motion and will prevent excessive resource use from looking for license plates in frames that don't even have a car passing through it.
Disable the improve_contrast motion setting, especially if you are running LPR at night and the frame is mostly dark. This will prevent small pixel changes and smaller areas of motion from triggering license plate detection.
Ensure your camera's timestamp is covered with a motion mask so that it's not incorrectly detected as a license plate.
For non-Frigate+ users, you may need to change your camera settings for a clearer image or decrease your global recognition_threshold config if your plates are not being accurately recognized at night.
The secondary pipeline mode runs a local AI model on your CPU or GPU (depending on how device is configured) to detect plates. Increasing detect fps will increase resource usage proportionally.

FAQ

Why isn't my license plate being detected and recognized?

Ensure that:

Your camera has a clear, human-readable, well-lit view of the plate. If you can't read the plate's characters, Frigate certainly won't be able to, even if the model is recognizing a license_plate. This may require changing video size, quality, or frame rate settings on your camera, depending on your scene and how fast the vehicles are traveling.
The plate is large enough in the image (try adjusting min_area) or increasing the resolution of your camera's stream.
Your enhancement level (if you've changed it from the default of 0) is not too high. Too much enhancement will run too much denoising and cause the plate characters to become blurry and unreadable.

If you are using a Frigate+ model or a custom model that detects license plates, ensure that license_plate is added to your list of objects to track. If you are using the free model that ships with Frigate, you should not add license_plate to the list of objects to track.

Recognized plates will show as object labels in the debug view and will appear in the "Recognized License Plates" select box in the More Filters popout in Explore.

If you are still having issues detecting plates, start with a basic configuration and see the debugging tips below.

Can I run LPR without detecting `car` or `motorcycle` objects?

In normal LPR mode, Frigate requires a car or motorcycle to be detected first before recognizing a license plate. If you have a dedicated LPR camera, you can change the camera type to "lpr" to use the Dedicated LPR Camera algorithm. This comes with important caveats, though. See the Dedicated LPR Cameras section above.

How can I improve detection accuracy?

Use high-quality cameras with good resolution.
Adjust detection_threshold and recognition_threshold values.
Define a format regex to filter out invalid detections.

Does LPR work at night?

Yes, but performance depends on camera quality, lighting, and infrared capabilities. Make sure your camera can capture clear images of plates at night.

Can I limit LPR to specific zones?

LPR, like other Frigate enrichments, runs at the camera level rather than the zone level. While you can't restrict LPR to specific zones directly, you can control when recognition runs by setting a min_area value to filter out smaller detections.

How can I match known plates with minor variations?

Use match_distance to allow small character mismatches. Alternatively, define multiple variations in known_plates.

How do I debug LPR issues?

Start with "Why isn't my license plate being detected and recognized?". If you are still having issues, work through these steps.

Start with a simplified LPR config.
- Remove or comment out everything in your LPR config, including min_area, min_plate_length, format, known_plates, or enhancement values so that the only values left are enabled and debug_save_plates. This will run LPR with Frigate's default values.
```
lpr:
  enabled: true
  device: CPU
  debug_save_plates: true
```
Enable debug logs to see exactly what Frigate is doing.
- Enable debug logs for LPR by adding frigate.data_processing.common.license_plate: debug to your logger configuration. These logs are very verbose, so only keep this enabled when necessary. Restart Frigate after this change.
```
logger:
  default: info
  logs:
    frigate.data_processing.common.license_plate: debug
```
Ensure your plates are being detected.

If you are using a Frigate+ or license_plate detecting model:
- Watch the debug view (Settings --> Debug) to ensure that license_plate is being detected.
- View MQTT messages for frigate/events to verify detected plates.
- You may need to adjust your min_score and/or threshold for the license_plate object if your plates are not being detected.
If you are not using a Frigate+ or license_plate detecting model:
- Watch the debug logs for messages from the YOLOv9 plate detector.
- You may need to adjust your detection_threshold if your plates are not being detected.
Ensure the characters on detected plates are being recognized.
- Enable debug_save_plates to save images of detected text on plates to the clips directory (/media/frigate/clips/lpr). Ensure these images are readable and the text is clear.
- Watch the debug view to see plates recognized in real-time. For non-dedicated LPR cameras, the car or motorcycle label will change to the recognized plate when LPR is enabled and working.
- Adjust recognition_threshold settings per the suggestions above.

Will LPR slow down my system?

LPR's performance impact depends on your hardware. Ensure you have at least 4GB RAM and a capable CPU or GPU for optimal results. If you are running the Dedicated LPR Camera mode, resource usage will be higher compared to users who run a model that natively detects license plates. Tune your motion detection settings for your dedicated LPR camera so that the license plate detection model runs only when necessary.

I am seeing a YOLOv9 plate detection metric in Enrichment Metrics, but I have a Frigate+ or custom model that detects `license_plate`. Why is the YOLOv9 model running?

The YOLOv9 license plate detector model will run (and the metric will appear) if you've enabled LPR but haven't defined license_plate as an object to track, either at the global or camera level.

If you are detecting car or motorcycle on cameras where you don't want to run LPR, make sure you disable LPR it at the camera level. And if you do want to run LPR on those cameras, make sure you define license_plate as an object to track.

It looks like Frigate picked up my camera's timestamp or overlay text as the license plate. How can I prevent this?

This could happen if cars or motorcycles travel close to your camera's timestamp or overlay text. You could either move the text through your camera's firmware, or apply a mask to it in Frigate.

If you are using a model that natively detects license_plate, add an object mask of type license_plate and a motion mask over your text.

If you are not using a model that natively detects license_plate or you are using dedicated LPR camera mode, only a motion mask over your text is required.

I see "Error running ... model" in my logs, or my inference time is very high. How can I fix this?

This usually happens when your GPU is unable to compile or use one of the LPR models. Set your device to CPU and try again. GPU acceleration only provides a slight performance increase, and the models are lightweight enough to run without issue on most CPUs.

Model Requirements​

Minimum System Requirements​

Configuration​

Advanced Configuration​

Detection​

Recognition​

Matching​

Image Enhancement​

Normalization Rules​

Debugging​

Configuration Examples​

Dedicated LPR Cameras​

Using a Frigate+ (or Native license_plate Detecting) Model​

Using the Secondary LPR Pipeline (Without Frigate+)​

Summary​

Best practices for using Dedicated LPR camera mode​

FAQ​

Why isn't my license plate being detected and recognized?​

Can I run LPR without detecting car or motorcycle objects?​

How can I improve detection accuracy?​

Does LPR work at night?​

Can I limit LPR to specific zones?​

How can I match known plates with minor variations?​

How do I debug LPR issues?​

Will LPR slow down my system?​

I am seeing a YOLOv9 plate detection metric in Enrichment Metrics, but I have a Frigate+ or custom model that detects license_plate. Why is the YOLOv9 model running?​

It looks like Frigate picked up my camera's timestamp or overlay text as the license plate. How can I prevent this?​

I see "Error running ... model" in my logs, or my inference time is very high. How can I fix this?​

Model Requirements

Minimum System Requirements

Configuration

Advanced Configuration

Detection

Recognition

Matching

Image Enhancement

Normalization Rules

Debugging

Configuration Examples

Dedicated LPR Cameras

Using a Frigate+ (or Native `license_plate` Detecting) Model

Using the Secondary LPR Pipeline (Without Frigate+)

Summary

Best practices for using Dedicated LPR camera mode

FAQ

Why isn't my license plate being detected and recognized?

Can I run LPR without detecting `car` or `motorcycle` objects?

How can I improve detection accuracy?

Does LPR work at night?

Can I limit LPR to specific zones?

How can I match known plates with minor variations?

How do I debug LPR issues?

Will LPR slow down my system?

I am seeing a YOLOv9 plate detection metric in Enrichment Metrics, but I have a Frigate+ or custom model that detects `license_plate`. Why is the YOLOv9 model running?

It looks like Frigate picked up my camera's timestamp or overlay text as the license plate. How can I prevent this?

I see "Error running ... model" in my logs, or my inference time is very high. How can I fix this?