Nowadays automatic number plate recognition is very much in use. Which sort of tool was first that business or any kind of organization used to control car parking, toll payment, accessibility to some particular region and buildings? Which first started with police and speed cameras. From a technical perspective it is easy to take out that license plate recognition is a technology based on computer vision and machine learning, which is a two type of AI technology.
Technology for recognizing license plates using computer vision and OCR
The analytical system translates images – still and moving – into characters that are intelligible for computers.Training models with large volumes of data – specifically those that fuel ML models – are required, especially those that are driving License Plate Detection Technology. Trained ALPR systems generate model training data in abundant quantities. Further, standalone ALPR solutions derive their prowess from optical character recognition (OCR) algorithms.
OCR is a difficult but powerful technology. Algorithms are capable of finding a corresponding image in a good base image database which stores millions of images. With the base database, the work of a human operator can be faster with high quality software which can handle some typeface, single color, two or more rows and fuzzy image. This algorithm is designed for a fast operation. There are six algorithms which work together to ensure the final answer.
Image Capture
With computer vision, objects in the frame can be automatically detected and processed, as the images and videos (stills or moving images) are taken. This is also suitable for face recognition.
Character division
Character segmentation allows for identifying all kinds of components. While it can segment a license plate image as a whole, it can cut apart an image of a number written on the plate only to find letters and digits, and separate characters with color, character spacing, structure, font and more features.
OCR
With that the acquired image is ready for transcribing into alphanumeric text. Now, the information can be verified by comparing the recognised number to the information that is stored in a database containing the text as the input.
Sizing, Rotation, And Normalization Of Images Sizing, Rotation, And Normalization Of Images
To circumvent these issues, the plate-reading algorithm needs to use some advanced, dedicated LPR software, improve the contrast and brightness of the captured image, and resize it into shape and size.
Geometric and Syntactic Analysis
From each number or character that it can recognise, it can map it into the appropriate classes. What information the algorithm is trying to probe out of the plate image will determine the resolution of the classes it defines.
These are the decisions comprising the pathways algorithms trace from a user to that result now having the ‘potential to accomplish a wide range of business goals’.
Conclusion
An automatic number plate recognition system or any kind of quality high-resolution image is needed. You need cameras adapted to the weather conditions that are stable, either fixed or mounted on a moving object, with a great view on the roadways and surroundings.
The client-oriented license plate recognition software is capable of reading number plates automatically by day or by night, with good visibility or bad visibility, and in all weather conditions. With already preprocessed image variations, optical character recognition works with images ready for use.
These systems can successfully handle video records and operate with videos as well.
