Carmen® GO ANPR software
Instant ANPR data from your traffic video stream (without integration)
- Plug & play ANPR solution
- Easy to use interface
- Built-in vehicle detection
License plate recognition technology is often referred to with abbreviations, such as ANPR – Automatic Number Plate Recognition, LPR – License Plate Recognition, ALPR – Automatic License Plate Recognition and more. While these abbreviations are slightly different, they all stand for an image processing software that is capable of recognizing license plates and transforming them to digital data.
ANPR or LPR? For further reading about the terminology, we suggest checking our blog article.
The operation of any ANPR system can be divided into three main steps.
At the front end of any ANPR system there is a camera that captures images of the license plates. The camera plays an important role in the recognition process by making sure that the captured images are optimal for ANPR purposes. This highly determines the overall performance of the system. The best ANPR results are achieved by using specialized cameras designed for ANPR. ARH offers a wide range of specialized ANPR cameras.
The main software aspect of an ANPR system is reading the plate text and identifying the type of the plate from the preselected set of captured images (ideally, only those images are analyzed that were pre-selected by the trigger system). Automated recognition includes several steps like image normalization and enhancement, detecting the vehicle in the image, then finding the plate on the image. The final step is done by the Optical Character Recognition algorithm that recognizes the individual characters and transforms them to digital text data.
For more information about image preselection and triggering, download our free e-book.
Besides the individual characters and the type of the vehicle plate, our CARMEN® ANPR also returns plenty of additional information, such as an image with the recognized plate(s) and the confidence level assigned to each character – as well as a confidence level for the whole plate. The result includes also the nationality and the color of the plates, with time-stamp and optionally geographical coordinates.
Once all license plate data haves been saved to a database, the data record serves as input to the end-user’s business logic. Automatic number plate recognition may be a key component of vehicle access control, traffic and toll enforcement and many other areas.
ANPR accuracy depends on two major contributing factors:
Higher quality input means a higher chance of 100% accuracy. In general, it can be stated that an image that is hard to recognize for humans will be similarly unlikely to be recognized by an ANPR system. The license plate on the captured images should not be too distorted, blurred or over-exposed. For this, we offer a complete guideline in our 24/7 ANPR image guide.
ANPR can be used in any road traffic environments: from parking houses to highways with a wide range of benefits. Access control systems use it for a license plate-based entry/exit control. Modern highways use ANPR for automated toll control. Traffic management systems use license plate recognition for identifying wanted vehicles, detecting speeders and violators. In smart cities, ANPR is used for congestion charging, journey time measurement (JTMS) and more. For more information, head to our relevant industry pages: Tolling systems, Traffic safety, Access control systems.
When checking ANPR software options, the following factors should be considered:
Depending on these factors, Carmen® offers ANPR software versions for 1-core, 2-core and 4-core CPU processing and various engines for local, regional and even global geographics.
With today’s contemporary deep learning technologies, ITS systems detect not only the category, weight or license plate but also the brand, model and color of the passing vehicles. Linking ANPR with make, model and color data comes particularly important when it is about to detect replaced or stolen license plates. Learn more about make and model recognition here.