This is an open source Automatic License Plate Recognition library written in C++ with bindings in C#, Java, Node.js, and Python. The library analyzes images and video streams to identify license plates. The output is the text representation of any license plate characters.
The software can be used in many different ways. For example,
- Recognize license plates from camera streams. The results are browsable, searchable, and can trigger alerts. The data repository can be in the cloud or stored entirely within your on-site network.
- Recognize license plates from camera streams and send the results to your own application.
- Process a video file and store the license plate results in a CSV and SQLite database.
- Analyze still images from the command-line
- Integrate license plate recognition into your application directly in-code (C/C++, C#, VB.NET, Java, Python, Node.js)
» Single pane-of-glass monitors license plates city-wide
» Operates independent of VMS applications
» Whitelist and Blacklist alerts
» Multiple user accounts with different access roles
» Visual verification to confirm license plate results
» Searchable database and historical graphs Easy to Integrate
» Simple API for embedding in applications
» Available both On-Premises and in the cloud
» Software runs natively on Linux and Windows
» APIs in C++, C, C#, VB.Net, Java, Python, and REST programming languages State-of-the-Art-Accuracy
» Greater than 99% accuracy on public, real-world benchmark
» Recognizes vehicles and license plates in over 60 countries
» Identifies issuing state for all 50 US states, Canada, and Mexico
» Recognizes vehicle color, make, and model Security and Insight
» City-wide License Plate Recognition with existing IP cameras
» Identify unauthorized vehicles on your property
» Monitor personnel activity entering and exiting company premises
» Identify VIP customers
» Ticketless parking lots garages & spaces
» Fleet tracking and auditing Real-Time Performance
» Real-time (30 fps) performance on multiple live camera streams
» Faster than real-time processing for video files
» Efficiently utilize available CPU and Nvidia GPU resources
» Distribute processing across multiple servers for large-scale deployments Save Money and Valuable Time
» Reuse existing IP cameras for LPR
» Significantly more affordable than legacy solutions
» Quickly search historic data to find matching vehicles
» Enhance efficiency of security staff
"This software turns any fixed or mobile IP camera into a state of the art LPR solution"