The Research and Improvement on Correction Algorithm of Inclination License Plate

2014 ◽  
Vol 543-547 ◽  
pp. 2800-2803
Author(s):  
Xiang Hua Hou ◽  
Hong Hai Liu
Keyword(s):  
Traffic Management ◽  
Character Segmentation ◽  
License Plate ◽  
License Plate Recognition ◽  
Correction Algorithm ◽  
Segmentation Algorithms ◽  
License Plate Detection ◽  
Transportation Technologies ◽  
Detection And Recognition ◽  
Improved Algorithm

More and more intelligent transportation technologies are applied to license plate detection and recognition that can greatly reduce the burden of traffic management. However, character segmentation of license plate is an indispensable step of license plate recognition. Traditional character segmentation algorithms of license plate mainly use the space between characters of license plate to segment characters, but the license plate cannot be recognized if there are degraded characters or license plate inclination. In this paper, an improved character segmentation algorithm of license plate is proposed. In the improved algorithm, firstly, the noise of precise located license plate is eliminated and then the license plate inclination is tested. When there is license plate inclination, we calculate the inclination angle and use rotation to correct the inclination. So, the problem of license plate inclination is solved in character segmentation. Lastly, the results show that the improved algorithm has better effect than the traditional algorithms and it lays a good foundation for the next research of license plate recognition.

scholarly journals Vehicle License Plate Detection and Perspective Rectification

2019 ◽  
Vol 25 (5) ◽  
pp. 47-56 ◽  
Author(s):  
Musaed Alhussein ◽  
Khursheed Aurangzeb ◽  
Syed Irtaza Haider
Keyword(s):  
Correction Method ◽  
Traffic Monitoring ◽  
Character Segmentation ◽  
Outdoor Environment ◽  
License Plate ◽  
Correction Algorithm ◽  
Vehicle Identification ◽  
Corner Points ◽  
Considerable Impact ◽  
License Plate Detection

The character segmentation and perspective rectification of Vehicle License Plate (VLP) is essential in different applications, including traffic monitoring, car parking, stolen vehicle recovery, and toll payment. The character segmentation of the VLP and its horizontal as well as vertical (pan and tilt) correction is a crucial operation. It has considerable impact on the precision of the vehicle identification process. In this work, we investigate an effective framework for the perspective rectification and homography correction of vehicle's images. The captured images of the vehicle could be tilted in vertical or horizontal or vertical-horizontal mix directions due to different movements. For reasonable high identification results, a polynomial fitting based homography correction method for rectifying the tilted VLPs is applied. A method for determining four corner points of the rotated VPLs is explored. These four detected corner points are applied in the homography correction algorithm. For comprehensively evaluating the performance of the proposed framework, the detected VLPs in various directions, such as horizontal, vertical, and mix horizontal-vertical, are rotated. For the experiments, the real images of the vehicles in the outdoor environment, from different directions and different distances are captured. With our proposed method, we achieve an accuracy of 97 % and 95 % for the simulated and real captured images, respectively.

Research on Pre-Processing Methods for License Plate Recognition

2021 ◽  
Vol 11 (1) ◽  
pp. 47-79
Author(s):  
Weifang Zhai ◽  
Terry Gao ◽  
Juan Feng
Keyword(s):  
Edge Detection ◽  
Mathematical Morphology ◽  
Traffic Management ◽  
Recognition System ◽  
Character Segmentation ◽  
License Plate ◽  
License Plate Recognition ◽  
Vertical Projection ◽  
License Plate Location ◽  
Plate Location

The license plate recognition technology is an important part of the construction of an intelligent traffic management system. This paper mainly researches the image preprocessing, license plate location, and character segmentation in the license plate recognition system. In the preprocessing part of the image, the edge detection method based on convolutional neural network (CNN) is used for edge detection. In the design of the license plate location, this paper proposes a location method based on a combination of mathematical morphology and statistical jump points. First, the license plate area is initially located using mathematical morphology-related operations and then the location of the license plate is accurately located using statistical jump points. Finally, the plate with tilt is corrected. In the process of character segmentation, the border and delimiter are first removed, then the character vertical projection method and the character boundary are used to segment the character for actually using cases.

scholarly journals Real-time Automated Detection and Recognition of Nigerian License Plates via Deep Learning Single Shot Detection and Optical Character Recognition

2021 ◽  
Vol 14 (4) ◽  
pp. 11
Author(s):  
Kayode David Adedayo ◽  
Ayomide Oluwaseyi Agunloye
Keyword(s):  
Real Time ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Character Segmentation ◽  
Detection Accuracy ◽  
License Plate ◽  
Single Shot ◽  
Optical Character ◽  
License Plate Detection ◽  
Detection And Recognition

License plate detection and recognition are critical components of the development of a connected Intelligent transportation system, but are underused in developing countries because to the associated costs. Existing license plate detection and recognition systems with high accuracy require the usage of Graphical Processing Units (GPU), which may be difficult to come by in developing nations. Single stage detectors and commercial optical character recognition engines, on the other hand, are less computationally expensive and can achieve acceptable detection and recognition accuracy without the use of a GPU. In this work, a pretrained SSD model and a tesseract tessdata-fast traineddata were fine-tuned on a dataset of more than 2,000 images of vehicles with license plate. These models were combined with a unique image preprocessing algorithm for character segmentation and tested using a general-purpose personal computer on a new collection of 200 automobiles with license plate photos. On this testing set, the plate detection system achieved a detection accuracy of 99.5 % at an IOU threshold of 0.45 while the OCR engine successfully recognized all characters on 150 license plates, one character incorrectly on 24 license plates, and two or more incorrect characters on 26 license plates. The detection procedure took an average of 80 milliseconds, while the character segmentation and identification stages took an average of 95 milliseconds, resulting in an average processing time of 175 milliseconds per image, or 6 photos per second. The obtained results are suitable for real-time traffic applications.

scholarly journals PVBR-Recog: A YOLOv3-based Brazilian Automatic License Plate Recognition Tool

2019 ◽  
Author(s):  
Pedro Ferreira Alves Pinto ◽  
Antonio José G. Busson ◽  
João P. Forny de Melo ◽  
Sérgio Colcher ◽  
Ruy Luiz Milidiú
Keyword(s):  
Deep Learning ◽  
License Plate ◽  
License Plate Recognition ◽  
Practical Applications ◽  
License Plate Detection ◽  
Detection And Recognition ◽  
Automatic License Plate Recognition

Vehicle’s license plate detection and recognition is a task with several practical applications. It can be applied, for example, in the security segment, identifying stolen cars and controlling cars entry/exit in private areas. This work presents a Deep Learning based tool that uses the cascaded YOLOv3 to simultaneously detect and recognize vehicle plate. In experiments performed, our tool got a recall of 95% in plate detection and 96.2% accuracy in the recognition of the 7 characters of the license plate.

Detection optimization of license plate targets based on AdvancedEAST

2021 ◽  
pp. 1-9
Author(s):  
Feifei Yin ◽  
Jingxuan Wang ◽  
Wei Xiong ◽  
Juanjuan Gao ◽  
Yu Gong
Keyword(s):  
Transfer Learning ◽  
Traffic Management ◽  
Recognition Rate ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Natural Scenes ◽  
License Plate ◽  
License Plate Recognition ◽  
Detection Model ◽  
License Plate Detection

As an important core in the intelligent traffic management system, the technology and application of license plate recognition have become research focus. Detecting the accurate location of a license plate from a vehicle image is considered to be the most crucial step of license plate recognition, which greatly affects the recognition rate and speed of the whole system. Nevertheless, due to the low accuracy of license plate detection in natural scenes, further investigations are still needed in this field in order to make the detection process very efficient. In this paper, We have studied and implemented a convolutional neural network license plate detection algorithm based on transfer learning. According to the invention, new energy license plates and ordinary license plates are adopted as the research objects. The text detection model AdvancedEAST is trained on the license plate images through the transfer learning method, and experiments are carried out on the self-built license plate dataset. The experimental results show that the algorithm can better adapt to light complexity, low resolution, target interference, license plate tilt and other complex conditions. The license plate positioning algorithm has high accuracy in natural scenes, and it is superior to the traditional license plate detection methods.

License plate detection and recognition based on YOLOv3 and ILPRNET

2021 ◽  
Author(s):  
Yongjie Zou ◽  
Yongjun Zhang ◽  
Jun Yan ◽  
Xiaoxu Jiang ◽  
Tengjie Huang ◽  
...  
Keyword(s):  
License Plate ◽  
License Plate Detection ◽  
Detection And Recognition

Application of Image Processing to the Vehicle License Plate Recognition

2013 ◽  
Vol 760-762 ◽  
pp. 1638-1641 ◽  
Author(s):  
Chun Yu Chen ◽  
Bao Zhi Cheng ◽  
Xin Chen ◽  
Fu Cheng Wang ◽  
Chen Zhang
Keyword(s):  
Traffic Engineering ◽  
Character Recognition ◽  
Character Segmentation ◽  
License Plate ◽  
License Plate Recognition ◽  
Edge Extraction ◽  
Recognition Method ◽  
License Plate Location ◽  
Vehicle License Plate Recognition ◽  
Plate Location

At present, the traffic engineering and automation have developed, and the vehicle license plate recognition technology need get a corresponding improvement also. In case of identifying a car license picture, the principle of automatic license plate recognition is illustrated in this paper, and the processing is described in detail which includes the pre-processing, the edge extraction, the license plate location, the character segmentation, the character recognition. The program implementing recognition is edited by Matlab. The example result shows that the recognition method is feasible, and it can be put into practice.

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