Vehicle License Plate Identification Research Articles

Character level vehicle license detection using multi layered feed forward back propagation neural network

Real-world traffic situations, including smart traffic monitoring, automated parking systems, and car services are increasingly using vehicle license detection systems (VLDS). Vehicle license plate identification is still a challenge with current approaches, particularly in more complicated settings. The use of machine learning and deep learning algorithms, which display improved classification accuracy and resilience, has been a significant recent breakthrough. Deep learning-based license plate identification using neural networks is proposed in this article. The number plate is detected using a multi layered feed forward back propagation neural network (MLFFBPNN). In this method, there are 3 layers namely input, hidden, and output layers has been utilized. Each layer has been related with interconnection weights. In feed forward of information, initially a set of randomly chosen weights are feed to the input data and an output has been determined. Back propagation training algorithm is utilized to train the network. Then character level identification is performed. The suggested proposed method is compared to the region-based convolutional neural network (RCNN) method in terms of accuracy and computational efficiency. The proposed method produced the character level recognition accuracy of 89%. It is improved by 4% when compared with the RCNN recognition method.

The Implementation Of Sound Output Image Processing With Wireless Based On Control To Vehicle Identification

Vehicle license plate identification is a system that utilizes image processing techniques to identify vehicles automatically from the vehicle license plate image. This system can be applied in various applications such as tracking stolen cars in homes or parking lots, automatic parking ticket payments, and collecting traffic statistics. Parking space as a facility to park motorists’ vehicles to be more organized. Parking space is currently still done conventionally. The development of technology, conventional control systems developed into electronic access systems, one of which is a monitoring system. The monitoring system using CCTV is a monitoring device, but it is expensive at the cost of electrical, network installation and limited mobility of device placement. In this study a Raspberry Pi-based room control system was designed. Raspberry Pi to build inexpensive monitoring systems and electricity-saving technology. This study aims to create a remote-based IP camera control system wirelessly for processing image plates of vehicle sound output with Raspberry Pi. The system starts by capturing the vehicle license plate with a Raspberry Pi-based camera or webcam. The control system is created using a motion program, and plate image processing uses digital image processing techniques and the Optical Character Recognition (OCR) method. The results of the system research can identify a Raspberry Pi server-based vehicle plate. The result is the vehicle license plate or text and voice output with text to speech technology. The results of implementing image processing from nine plate tests are 90.278%.

Internet of Things (IoT) as Green City Economic Development Smart Transportation System

The number of vehicles in Indonesia based on BPS data in 1987 amounted to 7.98148 million units to 104 118 969 units in 2013. An increase in the number of vehicles by 1304.51% is dominated by a motorcycle which was originally 5,554,305 units to 84,732,652 units. A large number of vehicles adds to problems of traffic regulation on the road. Internet of Things (IoT) can be used as the vehicle detection control device so as to create solutions Green Economic Development City Smart Transportation System that is good in handling motor vehicle. IoT sensor devices produced can identify the vehicles through model of information system connected with the detection system of motor vehicle license plate identification by means of sensors and data are stored in a digital chip which is mounted on any motor vehicle. With the chip of these vehicles will be produced a Green Economic Development City Smart Transportation System for the development of cities, especially in Narotama.

Efficient method for vehicle license plate identification based on learning a morphological feature

The detection and identification of license plates from captured images have been widely studied over the past two decades. The demand for this technology in security and commercial applications ranges from traffic control organisation to parking management and vehicle tracking. License plate recognition can be divided into two steps: detection and identification. Recent algorithms have focused on the detection step, with few researchers studying identification. In this study, an algorithm is developed to detect and identify license plates. The algorithm is based on the fact that license plates have a semi-symmetric distribution of corner points; it utilises morphological feature learning. The algorithm runs in real time, is highly robust, and can identify whether the candidate region contains a license plate.

A License Plate-Recognition Algorithm for Intelligent Transportation System Applications

In this paper, a new algorithm for vehicle license plate identification is proposed, on the basis of a novel adaptive image segmentation technique (sliding concentric windows) and connected component analysis in conjunction with a character recognition neural network. The algorithm was tested with 1334 natural-scene gray-level vehicle images of different backgrounds and ambient illumination. The camera focused in the plate, while the angle of view and the distance from the vehicle varied according to the experimental setup. The license plates properly segmented were 1287 over 1334 input images (96.5%). The optical character recognition system is a two-layer probabilistic neural network (PNN) with topology 108-180-36, whose performance for entire plate recognition reached 89.1%. The PNN is trained to identify alphanumeric characters from car license plates based on data obtained from algorithmic image processing. Combining the above two rates, the overall rate of success for the license-plate-recognition algorithm is 86.0%. A review in the related literature presented in this paper reveals that better performance (90% up to 95%) has been reported, when limitations in distance, angle of view, illumination conditions are set, and background complexity is low.