scholarly journals Erratum: License Plate Detection and Character Segmentation Using Adaptive Binarization Based on Superpixels under Illumination Change [IEICE Transactions on Information and Systems Vol.E100.D (2017) , No.6 pp.1384-1387]

2017 ◽  
Vol E100.D (7) ◽  
pp. 1554_e1-1554_e1
Author(s):  
Daehun KIM ◽  
Bonhwa KU ◽  
David K. HAN ◽  
Hanseok KO
Keyword(s):  
Character Segmentation ◽  
License Plate ◽  
Illumination Change ◽  
License Plate Detection

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.

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.

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: A SURVEY

2021 ◽  
Vol 16 (10) ◽  
Author(s):  
Tarun Kumar
Keyword(s):  
Character Recognition ◽  
Processing Technique ◽  
Character Segmentation ◽  
Image Processing Technique ◽  
License Plate ◽  
The Past ◽  
Pros And Cons ◽  
Three Stages ◽  
License Plate Detection ◽  
Future Direction

Automatic Number Plate Recognition (ANPR) is an image processing technique that is used to extract the symbols (characters and digits) embedded on the number (license) plate to identify the vehicles. Huge numbers of ANPR techniques have been proposed by various researchers in the past. Most of the ANPR techniques are designed for restricted conditions due to the diversity of the license plate styles, environmental conditions etc. Not every technique is suited for all kinds of conditions. In general, the ANPR technique comprises of the following three stages; license plate detection (LPD); character segmentation; and character recognition. There exist a wide variety of techniques for carrying out each of the steps of the ANPR. Some score over others. This paper presents a State-of-the-Art survey of the various leading LPD techniques that exist today and an attempt has been made to summarize these techniques based on pros and cons and their limitations. Each technique is classified based on the features used at each stage of LPD. This survey shall help provide future direction towards the development of efficient and accurate techniques for ANPR. It shall also assist in identifying and shortlisting the methodologies that are best suited for the particular problem domain.

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
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