scholarly journals A Fast and Noise Tolerable Binarization Method for Automatic License Plate Recognition in the Open Environment in Taiwan

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1374
Author(s):  
Chun-Cheng Peng ◽  
Cheng-Jung Tsai ◽  
Ting-Yi Chang ◽  
Jen-Yuan Yeh ◽  
Hsun Dai ◽  
...  
Keyword(s):  
Recognition Rate ◽  
Recognition System ◽  
License Plate ◽  
License Plate Recognition ◽  
Similar Time ◽  
Open Environment ◽  
Symmetry Principles ◽  
Otsu Method ◽  
Luminance Effect ◽  
Binarization Method

License plate recognition is widely used in our daily life. Image binarization, which is a process to convert an image to white and black, is an important step of license plate recognition. Among the proposed binarization methods, Otsu method is the most famous and commonly used one in a license plate recognition system since it is the fastest and can reach a comparable recognition accuracy. The main disadvantage of Otsu method is that it is sensitive to luminance effect and noise, and this property is impractical since most vehicle images are captured in an open environment. In this paper, we propose a system to improve the performance of automatic license plates reorganization in the open environment in Taiwan. Our system uses a binarization method which is inspired by the symmetry principles. Experimental results showed that when our method has a similar time complexity to that of Otsu, our method can improve the recognition rate up to 1.30 times better than Otsu.

Research of License Plate Recognition under Complex Environment

2014 ◽  
Vol 989-994 ◽  
pp. 2569-2575
Author(s):  
Feng Gao ◽  
Zhong Jian Dai ◽  
Kun Zhou ◽  
Ya Ping Dai
Keyword(s):  
Recognition Accuracy ◽  
Recognition Rate ◽  
Recognition System ◽  
License Plate ◽  
License Plate Recognition ◽  
Complex Environment ◽  
Color Information ◽  
Location Algorithm ◽  
Character Feature ◽  
Set Up

In order to improve the license plate recognition accuracy under complex environment, a new license location algorithm combining vertical edge detection, color information of the license plate and mathematical morphology is presented in this paper. For balance of computing load and recognition accuracy, a “200-d” character feature rule is designed, and the “200-d” feature is used as the input of BP neural network to recognize the characters. Based on the above-mentioned methods, a license plate recognition system is set up, which can locate and recognize the license plate effectively, even when the resolution of pictures and the position of vehicles in the pictures are not fixed. Experimental results indicate that the recognition rate of the algorithm reaches 90.5%.

scholarly journals Research and Implementation of Fast-LPRNet Algorithm for License Plate Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhichao Wang ◽  
Yu Jiang ◽  
Jiaxin Liu ◽  
Siyu Gong ◽  
Jian Yao ◽  
...  
Keyword(s):  
Traffic Management ◽  
Recognition Rate ◽  
Recognition System ◽  
Recognition Algorithm ◽  
License Plate ◽  
License Plate Recognition ◽  
Generalization Ability ◽  
Traffic Management System ◽  
High Recognition Accuracy ◽  
Fully Connected

The license plate recognition is an important part of the intelligent traffic management system, and the application of deep learning to the license plate recognition system can effectively improve the speed and accuracy of recognition. Aiming at the problems of traditional license plate recognition algorithms such as the low accuracy, slow speed, and the recognition rate being easily affected by the environment, a Convolutional Neural Network- (CNN-) based license plate recognition algorithm-Fast-LPRNet is proposed. This algorithm uses the nonsegment recognition method, removes the fully connected layer, and reduces the number of parameters. The algorithm—which has strong generalization ability, scalability, and robustness—performs license plate recognition on the FPGA hardware. Increaseing the depth of network on the basis of the Fast-LPRNet structure, the dataset of Chinese City Parking Dataset (CCPD) can be recognized with an accuracy beyond 90%. The experimental results show that the license plate recognition algorithm has high recognition accuracy, strong generalization ability, and good robustness.

Research on License Plate Recognition System Based on Computer Vision

2011 ◽  
Vol 65 ◽  
pp. 536-541
Author(s):  
Ye Qin Wang ◽  
Liang Hai Chen ◽  
Li Yun Zhuang
Keyword(s):  
Interface Design ◽  
Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Binary Image ◽  
License Plate ◽  
License Plate Recognition ◽  
Vertical Projection ◽  
Differential Operation ◽  
Program Transfer

In order to achieve the exact location and character recognition of license plate, firstly, this paper got binary image of license plate and done edge detection with differential operation. Secondly, it searched the license plate binary image after difference for the horizontal and vertical cut point, and determined the best cutting threshold through the experiment. Finally, it made the character segmentation by vertical projection, the recognition of license plate characters with the use of BP neural network, whose overall recognition rate is at 95.3%, and the display interface design for program transfer and results display. The experimental results showed that the location of license plate was exact and the character recognition rate was high.

A BACK PROPAGATION BASED REAL-TIME LICENSE PLATE RECOGNITION SYSTEM

2008 ◽  
Vol 22 (02) ◽  
pp. 233-251 ◽  
Author(s):  
YO-PING HUANG ◽  
TSUN-WEI CHANG ◽  
YEN-REN CHEN ◽  
FRODE EIKA SANDNES
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Recognition Rate ◽  
Back Propagation ◽  
Recognition System ◽  
Back Propagation Neural Network ◽  
License Plate ◽  
License Plate Recognition ◽  
Video Frames ◽  
Parking Lot

License plate recognition systems have been used extensively for many applications including parking lot management, tollgate monitoring, and for the investigation of stolen vehicles. Most researches focus on static systems, which require a clear and level image to be taken of the license plate. However, the acquisition of images that can be successfully analyzed relies on both the location and movement of the target vehicle and the clarity of the environment. Moreover, only few studies have addressed the problems associated with instant car image processing. In view of these problems, a real-time license plate recognition system is proposed that recognizes the video frames taken from existing surveillance cameras. The proposed system finds the location of the license plate using projection analysis, and the characters are identified using a back propagation neural network. The strategy achieves a recognition rate of 85.8% and almost 100% after the neural network has been retrained using the erroneously recognized characters, respectively.

scholarly journals An Adaptive Approach for Multi-National Vehicle License Plate Recognition Using Multi-Level Deep Features and Foreground Polarity Detection Model

2020 ◽  
Vol 10 (6) ◽  
pp. 2165 ◽  
Author(s):  
Muhammad Ali Raza ◽  
Chun Qi ◽  
Muhammad Rizwan Asif ◽  
Muhammad Armoghan Khan
Keyword(s):  
Character Recognition ◽  
Recognition Rate ◽  
Generalized Solution ◽  
Recognition System ◽  
Vital Role ◽  
Transport Systems ◽  
Support Vector ◽  
License Plate ◽  
License Plate Recognition ◽  
Detection Model

License plate recognition system (LPR) plays a vital role in intelligent transport systems to build up smart environments. Numerous country specific methods have been proposed successfully for an LPR system, but there is a need to find a generalized solution that is independent of license plate layout. The proposed architecture is comprised of two important LPR stages: (i) License plate character segmentation (LPCS) and (ii) License plate character recognition (LPCR). A foreground polarity detection model is proposed by using a Red-Green-Blue (RGB) channel-based color map in order to segment and recognize the LP characters effectively at both LPCS and LPCR stages respectively. Further, a multi-channel CNN framework with layer aggregation module is proposed to extract deep features, and support vector machine is used to produce target labels. Multi-channel processing with merged features from different-level convolutional layers makes output feature map more expressive. Experimental results show that the proposed method is capable of achieving high recognition rate for multinational vehicles license plates under various illumination conditions.

ENHANCING THE ACCURACY OF LICENSE PLATE RECOGNITION SYSTEMS WITH THE ANGLE RECOVERY METHOD

2013 ◽  
Vol 27 (08) ◽  
pp. 1350025
Author(s):  
WERNHUAR TARNG ◽  
CHIEN-LUNG LI
Keyword(s):  
Recognition Rate ◽  
Recognition System ◽  
Motor Vehicles ◽  
License Plate ◽  
License Plate Recognition ◽  
Plane Rotation ◽  
Recovery Method ◽  
Rotation Method ◽  
Wide Range ◽  
Recognition Systems

The motor vehicle is an important way of transportation for modern people, and its license plate is just like our identification cards which can be used for effective management of motor vehicles. Hence, the development of a recognition system for license plates can reduce the workload of managing motor vehicles. A license plate recognition system based on computer vision often causes recognition errors due to the plate's angle problem and thus needs to be assisted by manual recognition. In this study, a recovery method for license plate images with large angles is proposed based on perspective projection to increase the recognition accuracy. The experimental results show that applying the angle recovery method to a license plate recognition system can reduce its errors, especially for license plate images with large angles. For the case of small angles, a recognition rate of 89% can be achieved by the angle recovery method, slightly higher than that of the plane rotation method (86%). For the case of a wide range of different angles, the average recognition rate achieved by the angle recovery method is 87.5%, much higher than that of the plane rotation method (57.5%). Thus, the angle recovery method is effective for enhancing the accuracy of license plate recognition systems.

Automatic Extraction Method for License Plate Character Blurred by Translucent Object

2011 ◽  
Vol 204-210 ◽  
pp. 1884-1890
Author(s):  
Fang Fang Liang ◽  
Yong Liu ◽  
Xi Yan Wu ◽  
Gang Yao
Keyword(s):  
Recognition Rate ◽  
Closed Form Solution ◽  
Recognition System ◽  
Form Solution ◽  
Natural Image ◽  
License Plate ◽  
Automatic Extraction ◽  
License Plate Recognition ◽  
Blurred Image ◽  
Left And Right

Characters on license plate, which is obscured by water or mist, is hard to be recognized through classical license plate recognition technology. In order to get higher recognition rate of blurred character in practical license plate recognition system, an approach to separate characters from a blurred image of vehicle license plate is proposed in this paper. At first, the upper, lower, left and right boundaries of blurred character are assumed to be foreground sample; the broken boundary of character is considered as background sample. At the position of background white scribbles are drawn; in contrast, midpoints of the lines along foreground are painted with black scribbles. Then a scribble image is generated automatically. Furthermore, closed form solution to natural image matting is adopted to obtain the background that is uncovered license plate according to the scribble image. The experiments show that the new algorithm is applicable and helpful to get higher recognition rate of blurred license plate.

License Plate Location Research Based on Texture Analysis & Mathematics Morphology

2011 ◽  
Vol 317-319 ◽  
pp. 74-77 ◽  
Author(s):  
Yong Li Ma ◽  
Xuan Zeng ◽  
Jin Li
Keyword(s):  
Texture Analysis ◽  
Recognition Rate ◽  
Recognition System ◽  
License Plate ◽  
License Plate Recognition ◽  
A Algorithm ◽  
License Plate Location ◽  
Single Feature ◽  
Accurate Position ◽  
Plate Location

In the vehicle license plate recognition system, accurate position of license plate is a key step. By analyzing the texture of license plate, a algorithm based on texture analysis & mathematical morphology is proposed . Experimental results shows that this algorithm can overcome the shortcoming of low recognition rate caused by single feature algorithm, has good location effect.

scholarly journals Development of Iraqi License Plate Recognition System based on Canny Edge Detection Method

2020 ◽  
Vol 26 (7) ◽  
pp. 115-126
Author(s):  
Bydaa Ali Hussain ◽  
Mohammed Sadoon Hathal
Keyword(s):  
Edge Detection ◽  
Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Detection Algorithm ◽  
License Plate ◽  
License Plate Recognition ◽  
Canny Edge Detection ◽  
Canny Edge ◽  
On The Road

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.

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