License plate recognition depends more on controlled image geometry and exposure than on a camera's general resolution. Design the recognition view for plates first; use a separate overview camera when context is also required.
Choose a capture point
Select a location where vehicles follow a predictable path and speed. Keep horizontal and vertical angles shallow, fill enough of the frame with the plate, and confirm the specified recognition distance for the exact lens and camera model.
- Avoid aiming directly into sunrise, sunset, headlights, or reflective signs where possible.
- Account for vehicle lane position, gate queues, landscaping, and seasonal growth.
- Use a rigid mount; small movement can shift a tightly framed recognition zone.
Tune for motion and contrast
Set shutter speed to control motion blur, then balance gain and supplemental illumination. At night, verify that the plate is readable under real headlights; an attractive overview image may not be the best recognition image.
- Use the camera's dedicated plate or road mode as a starting point.
- Set focus at the actual capture line in both day and night conditions.
- Confirm country or region formats and confidence thresholds supported by the analytics.
Validate the complete workflow
Drive representative vehicles through every lane at realistic speeds and review recorded frames, not only live video. Check allowlist and alert behavior, retention, search accuracy, timestamps, and how operators correct a misread.
Field Note from GGIS
Collect test passes after dark and in wet weather. Retroreflective plates, glare, and water can expose settings that appear successful during a daytime ladder test.
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