License Plate Annotation Tool
A browser-based tool for turning vehicle footage into clean, reviewable license-plate datasets. It provides a guided workflow with useful defaults, so no computer-vision or command-line expertise is required after installation.
Assembled and packaged by hazen.ai, the tool combines existing open-source packages in an approachable interface. It is powered by FastALPR and uses ONNX models for plate detection and OCR.
Quick start
Python 3.10 or newer is required.
-
Install the package:
pip install license-plate-annotation-tool
To update an existing installation:
python -m pip install --upgrade license-plate-annotation-tool
-
Open the app:
license-plate-annotation-tool
The app opens automatically in your browser. If the launcher is not available in the current terminal, use:
python -m license_plate_annotation_tool
What it does
The guided workflow handles the complete process from a single uploaded video:
- Create frames — choose 5–20 FPS and optionally process only part of the video.
- Remove repeats — group visually similar frames and retain the clearest examples.
- Extract plates — detect and crop each plate, then use OCR text to name it when possible.
Each stage is also available independently when you only need one part of the workflow.
Results
Results are provided as downloadable ZIP files. Depending on the selected workflow, they include extracted frames, a reduced image set, or license-plate crops, together with:
manifest.csv— a record of generated files and processing outcomes.run_config.json— the settings used for that run.
Unreadable plates are still retained, allowing them to be reviewed and labeled manually.
Models
The default ONNX models are:
| Task | Default model | Provided through |
|---|---|---|
| Plate detection | yolo-v9-s-608-license-plate-end2end |
Open Image Models |
| Plate OCR | cct-s-v2-global-model |
Fast Plate OCR |
Other models exposed by the installed FastALPR version can be selected in the app. Model files are downloaded on first use, stored in the user's cache, and are not included in this package. CPU inference is provided by ONNX Runtime.
Privacy and storage
Uploads and generated files remain in a temporary directory for the active browser session. Download any results you want to keep before closing or restarting the app.
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