Nowadays, License Plate Recognition API technology is gaining popularity.
What first allowed businesses and different organizations to manage parking lots, collect tolls, and even provide access to particular regions and buildings.
What began with merely police and speed cameras.
From a technical perspective, license plate identification relies on computer vision and machine learning, two AI-related technologies.
Let’s examine this License Plate Recognition API technique in more detail.
The range of applications for License Plate Recognition API technology is enormous. This solution, for instance, can be applied in:
- monitoring stolen vehicles
- controlling parking lots
- enforcement of traffic
- Access management
- automatic collecting of tolls
Nowadays, parking facility operators, security firms, and other traffic control organizations use License Plate Recognition API in addition to the police. In this post, we’ll examine License Plate Recognition API in more detail and examine its operation. Let’s examine the background of License Plate Recognition API first.
Technology for recognizing license plates
In general, we can say that license plate recognition is a combination of two (or occasionally three) key AI-driven technologies:
- computer learning (sometimes also with deep learning)
- visual computing
License Plate Recognition API is essentially just a subset of OCR (Optical Character Recognition) software. Optical character recognition, or OCR, is a technology that enables you to turn various kinds of documents and images—like scanned paper documents, PDF files, or pictures taken with a digital camera—into editable and searchable data.
In our situation, license plates, which are made up of letters and figures, are of particular relevance. Every sign that can be located on a specific plate must be readable by the OCR program. There’s more, though.
Further, we require machine learning. Who knows? You’ll learn in a moment. Modern OCR tools can read and interpret a variety of fonts, colors, text rows and columns, and occasionally even blurry images.
How does License Plate Recognition API function?
Let’s use a simple example to illustrate this problem: a high school diploma. You can scan your diploma and upload the image to a computer, but since it’s still an image file (JPG, PNG, etc.), it can’t be edited. From your computer’s point of view, there aren’t any letters or numbers in this picture; just a few random pixels. Because of this, editing it requires a far more sophisticated graphics application. Of course, that needs effort, cannot be automated, and calls for a high level of expertise.
You can automate this process using License Plate Recognition API. You see, an OCR program can examine a standard image file, pick out the letters, put the letters together to form words, and then put the words together to form complete sentences. As a result, everything happens almost rapidly, and editing your scanned image doesn’t require any special graphic design skills. Now that your diploma has been read by a computer, it can locate your name and degree. With this approach, you can simultaneously access and edit the document’s content.
Let’s continue. The goal of the most sophisticated License Plate Recognition API systems is to mimic natural text recognition. How does it function? First, the observed item (in this case, a license plate) must be viewed as a single entity made up of several connected elements (digits and letters). Second, text extraction is the aim of our interpretation of the data in this situation.
And finally, the License Plate Recognition API algorithm needs to be able to learn on its own (and this is where machine learning steps into play).
Also published on Medium.