Car License Plate Recognition

Introduction

    Since the areas of machine learning and artificial intelligence get more and more fame and development many tasks of a specialist, in whatever domain he is, are getting either automated or at least machine assisted. In some domains of expertise we deal with images and sometimes images are a little hard and occasionally time-consuming to process, especially if we have some small unclear photos of a scene. What if the computer could process the image, deal with the little details, extract the information and abolish the manual insertion of that information. Since Optical Character Recognition is a thing we could extract a good deal of text from images without killing our eyes and trying to get a string out. However what if we use this thing on cars..?

From AI to Real Hardware

    The whole process of capturing a license plate using AI consists in different image processing phases which are: 

• Preprocess: preprocessing the captured image with different methods like: Gaussian Blur, Rotations, Enhancements, Projections, etc.
  
• Segmentation: finding the region in the picture where the license plate is.
  
• Recognition: Using the model to detect the letters and numbers on the plate in different fonts. Eventually syntactic rules can be used too.
  

     This whole process of using OCR (Optical Character Recognition) on Recognition of car license plates with all the preprocess and segmentation phases are popularly called Automatic Number-Plate Recognition (ANPR) or simply License-Plate Recognition (LPR) and it is mostly embedded in surveillance cameras which are placed in fixed points or on other vehicles to monitor the traffic or track cars wherever you need.

Motorola's L5F fixed LPR Camera (Left)
LPR Camera Mounted on a Police Car (Right)

We have the AI, we have the Hardware, now what?

    These system capable of detecting plate numbers have a good bunch of applications in the real world. As people are more and more depended on their cars we can apply these license plate detecting systems in:

• Security & Forensics: law enforcement uses such technology on their cameras. They can track stolen vehicles, wanted individuals, monitor the traffic ahead of the unit (speed-cameras).
• Vehicle Access Control: automated gate opening using license-plate recognition, associating plates with vehicles of persons in an organization and letting those in a certain space and not those from outside.
• Ticketless Parking: cameras scan for vehicles license plates to take different actions automatically in a parking lot. Vehicle Access Control comes into play and moreover we can have different automated tasks like sending alerts if the vehicle left the parking lot, to pay digitally without the need of emitting a ticket.

    The benefits this automation brings are numerous. For example, for the ticketless parking we could reduce substantially the waiting time because we could give up on emitting parking tickets, no need to wait for the barrier guy to open up, etc. Also if the computer can do all of this automatically you would not need human resources to open barriers and read tickets so this would be more cost effective. Thus the benefits of having License-Plate Recognition are cost and time efficiency. Moreover automated systems like this can leave more room for improvements (improved security, new features like alerts on smartphones, etc).

 

 

References:

• Survision, License Plate Recognition, URL: https://survisiongroup.com/post-what-is-license-plate-recognition, Last Accessed: 8.11.2022
• J. A. G. Nijhuis et al., "Car license plate recognition with neural networks and fuzzy logic," Proceedings of ICNN'95 - International Conference on Neural Networks, 1995, pp. 2232-2236 vol.5, doi: 10.1109/ICNN.1995.487708.
• Motoro Fixed License-Plated Recognition Camera Website, URL: https://www.motorolasolutions.com/en_us/video-security-access-control/license-plate-recognition-camera-systems/l5f-fixed-lpr-camera-story.html, Last Accessed: 9.11.2022
  
• Fu, Y. (2019). Automatic License Plate Recognition Using Neural Network and Signal Processing. UC Riverside. ProQuest ID: Fu_ucr_0032N_13706. Merritt ID: ark:/13030/m5cv9gq1. Retrieved from https://escholarship.org/uc/item/57q846r5