Abstract
Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended objective. Precise controlling and management of traffic conditions, increased safety and surveillance, and enhanced incident avoidance and management should be top priorities in smart city management. At the same time, Vehicle License Plate Number Recognition (VLPNR) has become a hot research topic, owing to several real-time applications like automated toll fee processing, traffic law enforcement, private space access control, and road traffic surveillance. Automated VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number plates. The current research paper presents an effective Deep Learning (DL)-based VLPNR called DL-VLPNR model to identify and recognize the alphanumeric characters present in license plate. The proposed model involves two main stages namely, license plate detection and Tesseract-based character recognition. The detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 model. Then, the characters in the detected number plate are extracted using Tesseract Optical Character Recognition (OCR) model. The performance of DL-VLPNR model was tested in this paper using two benchmark databases, and the experimental outcome established the superior performance of the model compared to other methods.
Highlights
There is a tremendous increase in the usage of vehicles in recent years, thanks to rapid economic growth of the country
The characters in the detected number plate are extracted by Tesseract Optical Character Recognition (OCR) model
It is inferred that the Deep Learning (DL)-Vehicle License Plate Number Recognition (VLPNR) model can clearly recognize the license plate number on all the images
Summary
There is a tremendous increase in the usage of vehicles in recent years, thanks to rapid economic growth of the country. Classical VLPNR models utilize Machine Learning (ML) models especially its hand-crafted features to represent the essential features that exist in vehicle license plate image. Vehicle license plate recognition is a process of detecting a homogeneous text region through the detection of characters straightaway from the image [7]. Though it is simple and rapid, the existing models produced minimum detection rate, since the features learned from the characters are not sufficient to identify every character present in the image. A4layer CNN-based models are in use to detect the text regions present in input image.
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