scholarly journals ELM-Based Indonesia Vehicle License Plate Recognition System

2021 ◽  
Vol 328 ◽  
pp. 02005
Author(s):  
Basuki Rahmat ◽  
Endra Joelianto ◽  
I Ketut Eddy Purnama ◽  
Mauridhi Hery Purnomo
Keyword(s):  
Extreme Learning Machine ◽  
Template Matching ◽  
Recognition System ◽  
Character Segmentation ◽  
License Plate ◽  
License Plate Recognition ◽  
Matching Method ◽  
Location Determination ◽  
Average Accuracy ◽  
Learning Machine

In this paper, a widely developed learning machine algorithm called Extreme Learning Machine (ELM) is used to recognize Indonesia vehicle license plates. The algorithm includes grayscale, binary, erosion, dilation and convolution processes, as well as the process of smearing, location determination and character segmentation before the ELM algorithm is applied. The algorithm includes one crucial and rarely performed technique for extraction of vehicle license plates, namely Smearing Algorithms. In the experimental results, ELM is compared with the template matching method. The obtained outcome of the average accuracy of both methods has the same value of 70.3175%.

Research on License Plate Recognition Based on Template Matching Method

2014 ◽  
Vol 668-669 ◽  
pp. 1106-1109 ◽  
Author(s):  
Hai Xiu Chen ◽  
Xiao Yu Ding
Keyword(s):  
Template Matching ◽  
Rapid Development ◽  
Recognition System ◽  
License Plate ◽  
License Plate Recognition ◽  
Matching Method ◽  
Computer Communication ◽  
Detection Technology ◽  
Recognition Result ◽  
Intelligent Detection

With the rapid development of transportation, more application of computer, communication and intelligent detection technology appeared in transportation field. The license plate recognition system based on template matching is researched in this paper. The preprocessing to the image such as binarization, tilt compensation and edge extraction has been finished, and then the positioning and segmentation of image are realized. After the normalization to the image, the license plate recognition result based on template matching method is obtained through experiments.

Research and Design of License Plate Recognition System

2014 ◽  
Vol 556-562 ◽  
pp. 2623-2627
Author(s):  
Feng Ran ◽  
Fa Yu Zhang ◽  
Mei Hua Xu
Keyword(s):  
Adaptive Learning ◽  
Character Recognition ◽  
Complete System ◽  
Recognition System ◽  
Morphological Processing ◽  
Character Segmentation ◽  
License Plate ◽  
License Plate Recognition ◽  
Research And Design ◽  
Speed And Accuracy

Introduce a complete system of license plate recognition: using morphological processing and priori knowledge of license plate to discern the location of license plate, accomplishing tilt correction through Radon transform, then fulfilling character segmentation of accurate positioning license plate by projection, finishing character recognition through BP neural network which was improved by the use of adaptive learning rate and momentum factor. With the programming and verification on Matlab experimental platform, experimental results show that we can have a preferable recognition speed and accuracy.

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.

Extraction of License Plate Region in License Plate Recognition System

2013 ◽  
Vol 441 ◽  
pp. 655-659
Author(s):  
Yuan Ning ◽  
Yao Wen Liu ◽  
Yan Bin Zhang ◽  
Hao Yuan
Keyword(s):  
Edge Detection ◽  
Mathematical Morphology ◽  
Recognition System ◽  
Intelligent Vehicle ◽  
License Plate ◽  
License Plate Recognition ◽  
Important Stage ◽  
System A ◽  
Average Accuracy ◽  
Extraction Algorithm

Extraction of License plate region is an important stage in the intelligent vehicle license plate recognition system. A practical license plate extraction algorithm based on edge detection and mathematical morphology is presented, the algorithm mainly consists of six modules: pre-processing, edge detection, binaryzation and denoising, morphology operation, filtration of connected regions, finding license plate region. From the experiments, the algorithm can detect the region of license plate quickly with 98% average accuracy of locating vehicle license plate region.

Research and Realization for Embedded License Plate Recognition System

2013 ◽  
Vol 411-414 ◽  
pp. 1015-1019
Author(s):  
Yuan Ning ◽  
Yao Wen Liu ◽  
Yan Bin Zhang ◽  
Hao Yuan
Keyword(s):  
Power Consumption ◽  
System Design ◽  
Character Recognition ◽  
Template Matching ◽  
Median Filter ◽  
Recognition System ◽  
License Plate ◽  
License Plate Recognition ◽  
Design Constraints ◽  
Recognition Phase

In this paper, the embedded license plate recognition system based on TMS320DM642 is researched. During the design, median filter, threshold, and morphology closing operations are used to obtain license plate region, then segmented into disjoint characters for the character recognition phase, where the template matching is used to identify the characters. Embedded License Plate Recognition System, being smaller, has less power consumption with respect to software based LPR systems. The resulting hardware is suitable for applications where cost, compactness, and efficiency are system design constraints.

A License Plate Recognition System Based on HSV Space in Natural Lighting

2012 ◽  
Vol 590 ◽  
pp. 421-426
Author(s):  
Zhi Bin Zhang ◽  
Zhan Liu ◽  
Yong Sheng Song ◽  
Hai Yue Wang ◽  
Guo Jun Tang
Keyword(s):  
Template Matching ◽  
Color Space ◽  
Recognition System ◽  
License Plate ◽  
License Plate Recognition ◽  
Hsv Color Space ◽  
Traffic Demand ◽  
Natural Lighting ◽  
Scanning Procedure ◽  
Matching Procedure

In recent years, with the development of modern traffic demand, the automobile license plate recognition technology has obtained more and more attentions. In this paper, the license plate in the vehicle image is located by extracting the color feature in HSV color space. And after binarizing the license plate image, the vertical scanning procedure is used to segment the license plate characters, and the template matching procedure are used to recognize the characters according to the similar degrees. Experimental results show that the system designed in this paper can effectively recognize the license plate in natural lighting, with the accuracy up to 95% for the Chinese characters, 90.4% for the numbers, 84.4% for the letters, and the time consumption being second level.

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