How ALPR Vue Detects and Reads License Plates

A plain-English explanation of the two-stage AI pipeline that detects and reads license plates entirely in your browser, with no server involved.

ALPR Vue uses two AI models running locally in your browser — one to find license plates in the image, and a second to read the characters on each plate. Both models run entirely on your device, so no image data is ever sent to a server. Every frame from your camera or uploaded file passes through this pipeline automatically.

The processing pipeline

1. Capture

   You start the camera or upload a photo or video file. When using the live camera, the app captures frames continuously at up to approximately 50 frames per second and feeds them into the detection pipeline.

2. Plate detection

   A YOLOv9 AI model scans each frame for license plate regions. It analyzes the entire frame in a single pass and draws a bounding box around any plate it finds.

3. Text recognition (OCR)

   Each detected plate region is cropped out of the frame and passed to a MobileViT v2 OCR model. This model reads the characters on the plate and assigns a confidence score to each one.

4. Quality check

   Each result is scored against four criteria: character length, mean confidence, minimum per-character confidence, and plate format. Only results with a combined quality score of 0.7 or higher are kept. Results that fall below this threshold are discarded silently.

5. Confirmation window

   A plate must be detected consistently before it is added to your history. In standard mode, the same plate must appear for 3 continuous seconds. If the detection confidence is very high (mean confidence ≥ 0.8), this window shortens to 1 second.

6. Grouping

   Once confirmed, new detections are compared against plates already in your history. If two plate readings are at least 80% similar (measured by Levenshtein string distance), they are grouped together as the same plate. This prevents minor OCR variations — such as a single misread character — from creating duplicate entries.

All processing runs in a dedicated Web Worker thread, separate from the browser’s main UI thread. This keeps the interface smooth and responsive even while the models are actively running.

Plate quality validation rules

Before a plate is saved to your history, it must pass all four of these criteria:

Criterion	Requirement
Length	4–10 characters
Mean confidence	≥ 0.7
Minimum character confidence	≥ 0.5 (no single character can fall below this)
Format	2–4 alphanumeric characters, an optional hyphen or space, then 2–4 more alphanumeric characters
Combined quality score	≥ 0.7 required to store the result

Each criterion carries a different weight in the combined score: mean confidence counts for 30%, format and minimum character confidence each count for 25%, and length counts for 20%. A plate must reach 0.7 overall to be accepted.