License Plate Reader

Overview

Development stage

DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

2025-01-07 (day precision)Source: 2025-01-07T00:00:00

Withheld from public reporting

Not reported

Mission

Topic area

Law Enforcement

Operational functions

Document processing and extraction, Image and video analysis Derived by keyword rules; see methodology

Problem the AI is intended to solve

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.

Expected benefits

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.

System outputs

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.

Technology

AI classification

Computer visionSource: Computer Vision: AI that processes and interprets visual data (e.g., images and videos).

System name(s)

Not reported

Custom-developed code

Yes

Public source code

Not reported

Sourcing

How it was built

Purchased from vendorSource: a) Purchased from a vendor

Vendor (as reported)

Amped

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

No

Privacy Impact Assessment

Not reported

Authorization to Operate

No

Demographic features

Not reported

Training and evaluation data

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.

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported