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Automatic Number Plate Recognition

Automated Number Plate Recognition to identify vehicles in real-time.

What is automatic number-plate recognition?

Automatic number-plate recognition (ANPR) uses computer vision technology to read license number plates on vehicles in video streams from standard CCTV cameras. State-of-the-art deep learning algorithms provide rapid and fully automated number plate detection and recognition in real-time.

A number plate recognition system is a key component of security and transportation systems for vehicle identification. Such systems have many purposes and can be used in large-scale computer vision systems by tollway authorities, parking authorities, and law enforcement.

Features of automatic number-plate recognition (ANPR)

Deep learning based number plate recognition uses deep neural networks to read multiple number plates in real-time video streams. The visual vehicle identification method is robust and applicable for large-scale use across many locations.

  • Automated vehicle detection in real-time videos from common CCTV surveillance cameras.
  • Detection of multiple number plates and reading of the number plate to obtain a text string that is queried against a database.
  • Deep learning models are robust to different angles, weather, and lighting conditions.
  • Edge AI enables distributed on-device machine learning is fully privacy-preserving.
  • Decentralized, real-time processing does not need to send visuals to the cloud and is robust to connection loss (offline).

The value of ANPR with Deep Learning

Video-based number-plate recognition with deep learning is important for a wide range of applications that require vehicle identification to increase safety and security, automation, and productivity.

  • Automation: Automatic vehicle identification with common security cameras. No additional sensors are required, given security cameras provide a clear image of the number plates.
  • Efficiency: High-performing deep learning algorithms allow reading multiple number-plates in parallel.
  • Save costs of manual personnel to watch multiple video streams of surveillance cameras.
  • Performance: Increase identification quality through fully automated, fast, and contactless vehicle identification.
  • Scalable: Computer vision is a highly scalable method to identify vehicles across multiple locations using numerous cameras.
Last updated
May 5, 2023
Requires Viso Suite

How to get started

This solution is built on Viso Suite, the all-in-one computer vision platform to deploy, run and scale the solution with powerful tools and infrastructure.

To get started, request a personal demo, we will put together a plan that includes all the services you need to get started.

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