A recognition method of Car License Plate Characters based on template matching using modified Hausdorff distance

2010 ◽  
Author(s):  
You-qing Zhu ◽  
Cui-hua Li
Keyword(s):  
Hausdorff Distance ◽  
Template Matching ◽  
License Plate ◽  
Recognition Method ◽  
Modified Hausdorff Distance

An Improved Character Recognition Algorithm for License Plate Based on BP Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 202-207 ◽  
Author(s):  
Zhong Qu ◽  
Qing-li Chang ◽  
Chang-zhi Chen ◽  
Li-dan Lin
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Template Matching ◽  
Recognition Algorithm ◽  
License Plate ◽  
Recognition Method ◽  
Neural Network Method ◽  
Identification Rate ◽  
Advantages And Disadvantages ◽  
Network Method

License plate character recognition is the basis of automatic license plate recognition (LPR) and it plays an important role in LPR. In this paper, we considered the advantages and disadvantages of the neural network method and proposed an improved approach of character recognition for license plates. In our approach, firstly, license plates were segmented into character pictures by using the algorithm which combines the projection and morphology. Secondly, with a focus on each character picture, recognition results determined by the calculation of the new recognition algorithm were as a reflection of the different features of every kind of character image. Then, character image samples were classified according to different light environment and character type itself. Finally, we used extracted features vectors to train the BP (error back propagation) neural network with adding noise relatively. Due to the influence of environmental factors or character images themselves will bring font discrepancy, font slant, stroke connection and so on, compared with template matching recognition method, neural network method has relatively great space to enhance the recognition effect. In the experiment, we used 1000 license plates images that had been successfully located. Of which, 11800 character images have been successfully identified, and the identification rate of our new algorithm is 91.2%. The experiment results prove that the improved character recognition method is accurate and highly consistent.

Automatic Ear Localization Using Entropy-Based Binary Jaya Algorithm and Weighted Hausdorff Distance

2021 ◽  
Vol 12 (1) ◽  
pp. 50-76
Author(s):  
Partha Pratim Sarangi ◽  
Abhimanyu Sahu ◽  
Madhumita Panda ◽  
Bhabani Shankar Prasad Mishra
Keyword(s):  
Hausdorff Distance ◽  
Skin Color ◽  
Template Matching ◽  
Search Space ◽  
Jaya Algorithm ◽  
Illumination Variation ◽  
Skin Segmentation ◽  
Benchmark Datasets ◽  
Edge Based ◽  
Modified Hausdorff Distance

This paper presents an automatic human ear localization technique for handling uncontrolled scenarios such as illumination variation, poor contrast, partial occlusion, pose variation, ear ornaments, and background noise. The authors developed entropy-based binary Jaya algorithm (EBJA) and weighted doubly modified Hausdorff distance (W-MHD) to use edge information rather than pixels intensity values of the side face image. First, it embodies skin segmentation procedure using skin color model and successively remove spurious and non-ear edges which reduces the search space of the skin regions. Secondly, EBJA is proposed to trace dense edge regions as probable ear candidates. Thirdly, this paper developed an edge based weight function to represent the ear shape along with for the edge based template matching using W-MHD to identify true ear from a set of probable ear candidates. Experimental results using publicly available benchmark datasets demonstrate the competitiveness of the proposed technique in comparison to the state-of-the-art methods.

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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