Performance improvement of the license plate recognition systems with algorithmic fusion approach

2011 ◽  
Author(s):  
Engin Tamer ◽  
Burak Cizmeci
Keyword(s):  
Performance Improvement ◽  
License Plate ◽  
License Plate Recognition ◽  
Recognition Systems ◽  
Fusion Approach

Research and Design of the License Plate Recognition Systems Based on ARM S3C2440

2013 ◽  
Vol 333-335 ◽  
pp. 2484-2488
Author(s):  
Zhen Tao Qin ◽  
Wu Nian Yang ◽  
Ru Yang
Keyword(s):  
Real Time ◽  
Intelligent Transportation ◽  
Recognition System ◽  
Experimental Results ◽  
Dynamic Monitoring ◽  
License Plate ◽  
License Plate Recognition ◽  
Hough Transformation ◽  
Recognition Systems ◽  
Research And Design

In order to meet the need of real-time and dynamic monitoring of intelligent transportation, a License Plate Recognition (LPR) System Based on ARM S3C2440 is introduced and a vehicle license recognition system is designed and realized. This thesis comparatively explains the tasks and problems and dose analytic research across all phases of the system. Image binary and slant rectification also be discussed, which are difficulty points in LPR. According to the study of the license plate images, we use hough transformation and image reverse rotation , a inclined rectification method was proposed. The experimental results show that the approach is excellent in the accuracy with rapid speed and is in the robustness.

scholarly journals System of license plate recognition considering large camera shooting angles

2021 ◽  
pp. 82-91
Author(s):  
Heorhii Kuchuk ◽  
Andrii Podorozhniak ◽  
Nataliia Liubchenko ◽  
Daniil Onischenko
Keyword(s):  
Semantic Processing ◽  
Search Algorithm ◽  
Histogram Equalization ◽  
Recognition System ◽  
License Plate ◽  
License Plate Recognition ◽  
Software And Hardware ◽  
Recognition Systems ◽  
Automatic License Plate Recognition

The system of automatic license plate recognition (ALPR) is a combination of software and hardware technologies implementing ALPR algorithms. It seems to be easy to achieve the goal but recognition of license plate requires many difficult solutions to some non-trivial tasks. If the license plate is oriented horizontally, uniformly lighted, has a clean surface, clearly distinguishable characters, then it’ll be not too difficult to recognize such a license plate. However, the reality is much worse. The lighting of each part of the plate isn’t equal; the picture from the camera is noisy. Besides, the license plate can have a big angle relative to the camera and be dirty. These obstacles make it difficult to recognize the license plate characters and determine their location on the image. For instance, the accuracy of recognition is much worse on large camera angles. To solve these problems, the developers of automatic license plate recognition systems use a different approach to processing and analysis of images. The work shows an automatic license plate recognition system, which increases the recognition accuracy at large camera angles. The system is based on the technology of recognition of images with the use of highly accurate convolutional neural networks. The proposed system improves stages of normalization and segmentation of an image of the license plate, taking on large camera angles. The goal of improvements is to increase of accuracy of recognition. On the stage of normalization, before histogram equalization, the affine transformation of the image is performed. For the process of segmentation and recognition, Mask R-CNN is used. As the main segment-search algorithm, selective search is chosen. The combined loss function is used to fasten the process of training and classification of the network. The additional module to the convolutional neural network is added for solving the interclass segmentation. The input for this module is generated feature tensor. The output is segmented data for semantic processing. The developed system was compared to well-known systems (SeeAuto.USA and Nomeroff.Net). The invented system got better results on large camera shooting angles.

Automatic Number Plate Goyal Recognition-Image Processing Technique

2016 ◽  
Vol 10 (2) ◽  
Author(s):  
Priti Rajvanshi ◽  
Vijaypal Singh Dhaka
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Pattern Recognition ◽  
Mathematical Morphology ◽  
Geometric Analysis ◽  
Processing Technique ◽  
Image Processing Technique ◽  
License Plate ◽  
License Plate Recognition ◽  
Recognition Systems

Automated Number Plate Recognition (ANPR) is also known as Automated License Plate Recognition (ALPR).Automatic Number Plate Recognition or ANPR is a technology that uses pattern recognition to ‘read’ vehiclenumber plates. The design of ANPR systems is a field of research in artificial intelligence and pattern recognition. The main goal of this paper is to study algorithmic and mathematical principles of automatic number plate recognition systems. ANPR can be used to store the images captured by the cameras as well as the text from the number plate and using mathematical morphology methods to detect the edges of the rectangular plate. Mathematical morphology is a part of digital image processing which is concerned withimage filtering and geometric analysis by using structuring elements (SE).

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.

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