Vehicle License Plate Recognition Using Neural Networks

2021 ◽  
Author(s):  
Dnyanisha Gondhalekar ◽  
Omkar Chalke ◽  
Siddharth Bansal ◽  
Soumi Banerjee
Keyword(s):  
Neural Networks ◽  
License Plate ◽  
License Plate Recognition ◽  
Vehicle License Plate 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.

scholarly journals The fast recognition of vehicle license plate based on the improved template matching

2018 ◽  
Vol 176 ◽  
pp. 01029 ◽  
Author(s):  
Zhixin Jiang ◽  
Zhengkui Lin ◽  
Jing Tang ◽  
Hao Li ◽  
You Menglu
Keyword(s):  
Fuzzy Number ◽  
Template Matching ◽  
Real System ◽  
License Plate ◽  
License Plate Recognition ◽  
Slow Speed ◽  
Recognition Time ◽  
Fast Recognition ◽  
Vehicle License Plate Recognition

In order to solve the problem of low accuracy and slow speed in vehicle license plate recognition, a method of number-plate recognition using template matching is proposed. It can effectively recognize low quality and fuzzy number-plate image in real system .The accuracy is 95%, and the recognition time is close to 0.14s.

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