The popularity of systems that scan for license plates is growing in the modern day. What began as a tool for law enforcement and speed enforcement is now widely used to help businesses and government agencies control traffic, collect tolls, and even let people in and out of buildings. The two main AI-related technologies that underpin 3d printed number plates recognition are machine learning and computer vision. Let’s investigate the license plate reading equipment in more detail. License-plate reading technology has a wide variety of potential applications. This approach, for instance, has applications in the following:
- Recovering stolen automobiles
- Facility management for parking garages
- Controlling Traffic
- Limiting entry
- Traffic taxation by machine
- These days, parking lot attendants, security firms, and government organizations all utilize license plate readers as a tool for traffic management. In this piece, we’ll examine license plate recognition technologies in further detail. Let’s start with an examination of license plate recognition’s inner workings.
Car number plate scanning technology
We may generally characterize license plate recognition as a combination of two (and occasionally three) distinct AI-powered technologies:
Automated learning (sometimes also with deep learning)
Simply put, license plate recognition is an optical character recognition (OCR) application. In a nutshell, Optical Character Recognition (OCR) is a technique that allows you to modify and search data from scanned paper documents, PDF files, and digital camera photographs. Letters and numbers on license plates are of great significance to us. The OCR program must be able to decipher all the characters on a given plate. However, this is not all. Also, we’ll need some kind of machine learning system. And why? In a moment, you’ll know the answer. Modern OCR software is able to decipher and analyze documents with wildly varying fonts, colors, text alignments, and even blurry photos.
How OCR works?
Let’s use an easily understandable example—a high school diploma—to illustrate the problem. The picture of your diploma that you scan into a computer will be a standard image file (JPG, PNG, etc.) and will not allow for any changes to be made. From the viewpoint of your computer, this image contains just a bunch of meaningless pixels, rather than text or numbers. For this reason, a more sophisticated graphics editing application is required. That, of course, is labor-intensive, cannot be mechanised, and requires high levels of expertise.
Then let’s go. Recent developments in OCR technology have centered on improving upon the system’s ability to simulate human-level text recognition. What is the procedure? As a first step, it’s important to recognise that the observed item (a licence plate in this example) is a unified whole made up of numerous subcomponents (digits and letters). Secondly, there is always a reason for interpreting data (here, text extraction).
Take pictures
In this respect, computer vision is crucial. The act of acquiring and analyzing visual data is known as “image acquisition.” Computer vision applications often follow a set of predetermined rules while searching for patterns or detecting items in a picture. This time, we need your help locating automobiles and their license plates. The question is, what happens if there are many cars in the frame? All license plates in view should be read using a good OCR program.
Locating objects in pictures
Step two is to “localize” (identify) the significance of individual pixels within the picture. Several factors, including camera placement, camera angle, vehicle speed and direction, and vehicle orientation, must often be taken into account. Finally, how about the license plates themselves?
Things are broken down into their simplest forms here. You see, everyone can recognize them since they all use the same basic design and typeface. On the other hand, surely a vehicle may have more than one square component. Since the shape and other parameters used by OCR software to accurately identify a license plate aren’t enough, the algorithm has to be taught to account for additional information.
Equalizing images
We’ve established that not every picture will have the same clarity or perspective. In order to assist the text extraction process, the OCR system must scale and crop these photos to the ideal dimensions. Let’s pretend the algorithm is able to identify a number of cars from a variety of perspectives simultaneously. Our system must read and adjust the contrast and brightness of an image to find all the license plates in it. There is no human involvement; everything is done mechanically.
Furthermore, for obvious reasons, the picture of the moving automobile is frequently significantly more complex to analyze than the image of a stationary vehicle. You’re aware that the algorithm must additionally consider factors like the current weather and the time of day. The normalization process is where all of it occurs with a picture.
An Image-to-Text Converter
The next step is to break apart the license a plate characters. Understand that the OCR system does not do a comprehensive readout of the license plate. Not at all; the numbers and letters are represented by distinct symbols. Parameters such as the font’s color, character distance, and structure are used to decipher the license plate number into letters and digits.
