Imagine how useful it can be to control the entry and exit of vehicles from a territory only with the use of this License Plate Recognition API in conjunction with the local monitoring camera system.
This function is extremely useful to control if a certain vehicle enters the territory in question and through the use of network cameras on the street we can even follow each step in the route of this vehicle in question.
Not only that, it is also useful to keep a count of the daily traffic within the territory or that passes through a particular radius. Checking the number of vehicles and their license plates has never been as simple as with this License Plate Recognition API.
But not only do you have to restrict yourself to controlling which vehicles enter the monitored territory, with this License Plate Recognition API you can also know when and by which route they left.
So, if this License Plate Recognition API gives us this information on the entry and exit of the territory of a vehicle or several (there is no data limit to process) we can also know how long this vehicle was within the territory and added to the street cameras it can be generated a map of the route made during the time that this visit in question lasted.
How does this License Plate Recognition API works?
The software of this License Plate Recognition API requires seven primary algorithms for identifying a license plate:
- Plate localization is in charge of locating and isolating the plate on the image.
- Plate orientation and sizing – compensates for plate skew and adjusts dimensions to required size
- Normalization is the process of adjusting the brightness and contrast of an image.
- Character segmentation identifies the various characters on the plates.
- Recognition of optical characters
- Check characters and positions against country-specific rules using syntactical/geometrical analysis.
- Averaging the recognized value across multiple fields/images to produce a more reliable or confident result, especially since any single image may contain a reflected light flare, be partially obscured, or have other obfuscating effects.
The accuracy of the License Plate Recognition API system is determined by the complexity of each of these program subsections. Some systems use edge detection techniques during the third phase (normalization) to increase the image difference between the letters and the plate backing. A median filter can also be used to reduce image visual noise.
Of course, there are numerous issues that could complicate the License Plate Recognition API process, but some of these issues can be resolved within the software, it is primarily up to the hardware side of the system to find solutions. Increasing the camera’s height may avoid problems with objects (such as other vehicles) obscuring the plate, but it introduces and exacerbates other issues, such as adjusting for the plate’s increased skew.
Finally, I would like to mention that this License Plate Recognition API offers a considerable variety of plans so that you can adopt the one that best suits your needs.
Also published on Medium.