A Real-Time Automatic Plate Recognition System Based on Optical Character Recognition and Wireless Sensor Networks for ITS

Abstract

Automatic License Plate Recognition has been a recurrent research topic due to the increasing number of cameras available in cities, where most of them, if not all, are connected to the Internet. The video traffic generated by the cameras can be analyzed to provide useful insights for the transportation segment. This paper presents the development of an intelligent vehicle identification system based on optical character recognition (OCR) method to be used on intelligent transportation systems. The proposed system makes use of an intelligent parking system named Smart Parking Service (SPANS), which is used to manage public or private spaces. Using computer vision techniques, the SPANS system is used to detect if the parking slots are available or not. The proposed system makes use of SPANS framework to capture images of the parking spaces and identifies the license plate number of the vehicles that are moving around the parking as well as parked in the parking slots. The recognition of the license plate is made in real-time, and the performance of the proposed system is evaluated in real-time.

Scale-Invariant Multidirectional License Plate Detection with the Network Combining Indirect and Direct Branches

“…Both detection branches can detect multidirectional license plates by regressing the four corners of the license plate. After tilt correction, we can improve the license plate recognition performance [ 19 , 20 , 21 , 22 ]. The license plate information can be applied to barrier access control [ 20 , 22 ], vehicle target detection [ 49 ], vehicle re-identification [ 50 ], etc.…”

“…Moreover, many methods [ 6 , 9 , 10 , 11 , 16 , 18 ] do not consider the orientation of the license plate, which is only applicable to specific scenarios, such as parking charges and vehicle access/exit management. When it comes to more complex scenarios, such as road scenes, if we regard the tilted license plate as the horizontal direction, it may cause errors in the subsequent license plate recognition [ 19 , 20 , 21 , 22 ]. Although Dong et al [ 14 , 23 ] propose to detect the multidirectional license plate, these methods are very complicated due to adopting multiple separate models.…”

A Character Segmentation Method to Increase Character Recognition Accuracy for Turkish Number Plates

“…ANPR systems and the OCR phase of this system are among the most widely studied problems in pattern recognition and computer vision [27]. Despite being widely studied, these topics remain challenging problems when used in unconstrained environments [2]. The challenging problems which arise in this domain are listed and explained below:…”

“…The advancement of technology has triggered much interest among researchers to study the computer vision techniques used in ANPR systems. The grow up of smart city applications also supported this increase [2]. This market's growth is driven by the rise in demand from traffic surveillance applications and infrastructure growth in emerging economies [3].…”

“…The diversity of number plate formats is caused by several features, such as plate sizes, plate backgrounds, character sizes, plate textures, etc. [2]. For better recognition of number plates, intelligent algorithms are required [10].…”