License Plate Reader
Description
To identify license plate information quickly, efficiently, and accurately from low-quality imagery, advanced image processing and machine learning techniques are typically employed. Need to be able to enhance the clarity of the image, correct distortions, and extract relevant details, even in challenging conditions such as low resolution, poor lighting, motion blur, or obstructions.
Detailed example
Optical Character Recognition (OCR) technology is utilized to detect and interpret the alphanumeric characters on the license plate. The extracted license plate information serves as a decision support tool, aiding investigators by highlighting possible characters and combinations. This allows investigators to efficiently generate and verify leads while complementing their independent analysis.
AI / analytics pattern
Computer Vision: AI that processes and interprets visual data (e.g., images and videos).
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
The purpose of identifying license plate information from low-quality imagery is to enable accurate and efficient vehicle identification for applications such as law enforcement, traffic monitoring, parking management, border security, and access control, ensuring operational efficiency and enhanced security.
Controls / human review
ATO: No; PIA: Not published
Data needed
The vendor, trained a dedicated neural network with millions of synthetically generated and distorted license plates for several countries/states. No license plate images were scraped from the web. Experimental validation was performed by the vendor on Italian license plates. Neural Network for Denoising and Reading Degraded License Plates.