Query: License plate recognition based on Neural Network

title: Automatic Vehicle License Plate Recognition Using Optimal K Means With Convolutional Neural Network for Intelligent Transportation Systems
abstract: Due to recent developments in highway research and increased utilization of vehicles, there has been significant interest paid on latest, effective, and precise Intelligent Transportation System (ITS) The process of identifying particular objects in an image plays a crucial part in the fields of computer vision or digital image processing. Vehicle License Plate Recognition (VLPR) process is a challenging process because of variations in viewpoint, shape, color, multiple formats and non uniform illumination conditions at the time of image acquisition. This paper presents an effective deep learning based VLPR model using optimal K means (OKM) clustering based segmentation and Convolutional Neural Network (CNN) based recognition called OKM CNN model. The proposed OKM CNN model operates on three main stages namely License Plate (LP) detection, segmentation using OKM clustering technique and license plate number recognition using CNN model. During first stage, LP localization and detection process take place using Improved Bernsen Algorithm (IBA) and Connected Component Analysis (CCA) models. Then, OKM clustering with Krill Herd (KH) algorithm get executed to segment the LP image. Finally, the characters in LP get recognized with the help of CNN model. An extensive experimental investigation was conducted using three datasets namely Stanford Cars, FZU Cars and HumAIn 2019 Challenge dataset. The attained simulation outcome ensured effective performance of the OKM CNN model over other compared methods in a considerable way.

title: Fast License Plate Recognition Method Based on Competitive Neural Network
abstract: Vehicle license plate recognition is an important part of Intelligent Transportation Network. Generally speaking, the traditional LPR mainly consists of three parts: location, segmentation and recognition. Recognition is the most important part of LPR and the effect of recognition is closely related to the result of location segmentation. The accuracy of recognition is the most important index to test the effect of recognition. This paper introduces the recognition method of license plate characters by competitive neural network, and discusses a fast recognition method of the number and letter of vehicle license plate under natural background by using an improved competitive neural network. The results show that the character recognition rate of the images taken under the natural landscape is 91.3% Keywords License plate recognition; neural network; character detection)

title: A Robust Method for Vehicle License Plate Recognition based on Harries Corner Algorithm and Artificial Neural Network
abstract: Automatic vehicle license plate recognition system plays an important role in Intelligent Transportation System. Even though many license plate recognition methods have been proposed in the past, further scope for improvement still exists. This paper has proposed a new approach to license plate recognition system based Harris corner detection algorithm. In this paper connected component analysis is used for character segmentation and artificial neural networks for character recognition. It is observed from the experiments that the developed system successfully identifies and recognizes the vehicle number plate under different illumination conditions and independent of orientation and scale of the

title: Research and Implementation of License Plate Character Recognition Technology Based on Neural Network
abstract: Technology Based on Neural Network Chong Xu, Xin Liu,Wei Wang,Yong Shiand Ning Tao 1 BIM Computing Research Center, Shenyang jianzhu University, Shengyang, 110168, China 2 BIM Computing Research Center, Shenyang jianzhu University, Shengyang, 110168, China 3 BIM Computing Research Center, Shenyang jianzhu University, Shengyang, 110168, China 4 BIM Computing Research Center, Shenyang jianzhu University, Shengyang, 110168, China 5 BIM Computing Research Center, Shenyang jianzhu University, Shengyang, 110168, China 32500103@qq.com, 6620274@qq.com, 519836646@qq.com, 674365154@qq.com, 23010150@qq.com The Corresponding Author

title: License Plate Recognition in Urban Road Based on Vehicle Tracking and Result Integration
abstract: Multiple surveillance cameras provide huge video resources that need further mining to collect traffic stream data such as license plate recognition (LPR) However, these surveillance cameras have limited spatial resolution, which may not always suffice to precisely recognize license plates by existing LPR systems. This work is focused on the LPR method in low quality images from surveillance video screenshots on urban road. The methodology we proposed is based on vehicle tracking and result integration, and we recognize the plate with an end to end method without character segmentation. First, we track each vehicle to get vehicle tracking sequence. Moreover, a plate detector based on an object detection framework is trained to detect license plates of each vehicle from the sequence and a license plate sequence is formed. In addition, an end to end convolutional neural network architecture is applied to recognize license plates from the sequence. Finally, we integrate the recognition result of continuous frames to get the final result. Evaluation results on multiple datasets show that our method significantly outperforms others without segmentation or integration in real traffic scene.

title: Development of Iraqi License Plate Recognition System based on Canny Edge Detection Method
abstract: In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the road in all the sections of the country. Vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the developing system is consist of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny Edge detection algorithm, Connect Component Analysis (CCA) have been exploited for segmenting characters. Finally, a Multi Layer Perceptron Artificial Neural Network (MLPANN) model is utilized to recognize and detect the vehicle license plate characters, and hence the results are displayed as a text on GUI. The proposed system successfully identified and recognized multi_style Iraqi license plates using different image situations and it was evaluated based on different metrics performance, achieving an overall system performance of 91.99% This results shows the effectiveness of the proposed method compared with other existing methods, whose average recognition rate is 86% and the average processing time of one image is 0.242s which proves the practicality of the proposed method.

title: An Improved Character Recognition Algorithm for License Plate Based on BP Neural Network
abstract: License plate character recognition is the basis of automatic license plate recognition (LPR) and it plays an im portant role in LPR. In this paper, we considered the advantages and disadvantages of the neural network method and pro posed an improved approach of character recognition for license plates. In our approach, firstly, license plates were seg mented into character pictures by using the algorithm which combines the projection and morphology. Secondly, with a focus on each character picture, recognition results determined by the calculation of the new recognition algorithm were as a reflection of the different features of every kind of character image. Then, character image samples were classified according to different light environment and character type itself. Finally, we used extracted features vectors to train the BP (error back propagation) neural network with adding noise relatively. Due to the influence of environmental factors or character images themselves will bring font discrepancy, font slant, stroke connection and so on, compared with template matching recognition method, neural network method has relatively great space to enhance the recognition effect. In the experiment, we used 1000 license plates images that had been successfully located. Of which, 11800 character images have been successfully identified, and the identification rate of our new algorithm is 91.2% The experiment results prove that the improved character recognition method is accurate and highly consistent.

title: License Plate Detection and Recognition using Vertical based Edge Detection Algorithm and Radial Basis Function Neural Network
abstract: The projected aspects in this paper constitute the powerful means of a well organized and an automated exposure and perception of license plates.  This procedure exhibits an esteemed Vertical Based Edge Detection Algorithm and a Radial Basis Function Neural Network Algorithm for the revelation and disclosure of license plates. After the image procurement, the entity starts with certain primitive pre processing steps. Consequently, the vertical edges are disclosed by adopting Vertical Edge Detection Algorithm and the number plates are identified and separated using the Structured Component Analysis. Lastly, the characters inside the License Plates are disjointed and discovered by applying Connected Component Labeling and Radial Basis Function Neural Network Algorithm.  The intended technique pinpoints the vehicle number plates and distinguishes the characters in 90.76% photographs fruitfully.  Focuses on the Intelligent Transportation System, which strengthens shipping capacity, security and flexibility.

title: Design of a Recognition System Automatic Vehicle License Plate through a Convolution Neural Network
abstract: The present work is a study on the practical application of Learning process (Deep Learning) in the development of a system of Automatic recognition of vehicle license plates. These systems commonly referred to as ALPR (Automatic License Plate Recognition) are able to recognize the content of vehicles from the images captured by a camera. The system proposed in this work is based on an image classifier developed through supervised learning techniques with convolution neural network. These networks are one of the most profound learning architectures and are specifically designed to solve artificial vision, such as pattern recognition and classification of images. This paper also examines basic processing techniques and Image segmentation such as smoothing filters, contour detection necessary for the proposed system to be able to extract the contents of the license plates for further analysis and classification. This paper demonstrates the feasibility of an ALPR system based on a convolution neural network, noting the critical importance it has to design a network architecture and training data set appropriate to the problem to be solved.

title: The Improved Neural Network Algorithm of License Plate Recognition
abstract: With high definition cameras are widely used in urban traffic monitoring, the resolution of vehicle and traffic monitoring image rises. As the main component of intelligent traffic system, the vehicle license plate recognition technique faces new challenges and some new demands are put forward. Aiming at the problems of cutting down the time delay and eliminating characters outside of vehicle license plate, a set of license plate recognition algorithm is developed in this paper. The process of recognition in this paper is divided into four parts: image preprocessing, license plate location, license plate segmentation and character recognition. In license plate location, an accelerating matching method based on particle image correlation method is employed. In character recognition, an improved BP neural network algorithm is employed. The principle, procedure and simulation of the algorithm is described in details. High definition images with 2 mega pixels are used as the input of simulation.