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.

scholarly journals License Plate Recognition with Feature Salience and Neural Network

2019 ◽  
Vol 8 (10) ◽  
pp. 2985-2990
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Recognition Algorithm ◽  
License Plate ◽  
License Plate Recognition ◽  
Core Component ◽  
Character Extraction ◽  
Recognition Result

Character recognition algorithm is considered as a core component of License Plate Recognition (LPR) systems. Numerous methods for License Plate (LP) recognition have been developed in recent years. However, most of them are not advanced enough to recognize in complex background and still demand improvement. This paper introduces a novel system for LPR by analyzing vehicle images. Accurate segmentation of license plate and character extraction from the plate is accomplished. In the plate segmentation module, Hough transform is put forwarded to identify plate edges using line segments. Radon transform adjusts the skew between LP and the viewer, thereby improve the recognition result. Four features are extracted from the LP image, and best features are selected using feature-salience theory. Histogram projection is performed horizontally and vertically to isolate individual characters in the LP. Finally, Back Propagation Neural Network (BPNN) is used to identify the characters present in the LP. From experimental results, it is evident that the proposed system can recognize LP more efficiently and establish a good background for future advancements in LPR.

Character Pattern Recognition Algorithm Based on Principal Component Analysis

2012 ◽  
Vol 433-440 ◽  
pp. 5313-5318
Author(s):  
Feng Xian Tang
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Character Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition Algorithm ◽  
License Plate ◽  
Specific Work ◽  
Advantages And Disadvantages

With the help of the study on mathematical theory and its progress and the development of the computer techniques, digital image processing technology has more and more been applied in each field. The pattern recognition judges unknown things by substituting machine for human eyes, which has a high application value. Thus, it becomes the major branch in image processing fields. The character recognition technology has developed rapidly because of its broad application prospect. Until now, it has been applied successfully in OCR and vehicle license plate recognition. However, it has certain difficulty for the pattern recognition to meet the specific requirements related to specific work scenes. This essay discusses several Eigen value selecting approaches and analyzes the advantages and disadvantages of each. For the template matching methods with penalty factors, in design, character recognition algorithm based on the principal component analysis is realized where scattering matrix between classes is as produced matrix.

Character Recognition Based on Wavelet Neural Network Optimized with PSO Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 1834-1837 ◽  
Author(s):  
Song Chi
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Gradient Descent ◽  
Pso Algorithm ◽  
Wavelet Neural Network ◽  
License Plate ◽  
Recognition Method ◽  
Invariant Moments ◽  
Gradient Descent Algorithm ◽  
Simulation Results

To improve the performance of the character recognition based on wavelet neural network (WNN) with gradient descent algorithm, a new character recognition method based on WNN optimized by PSO algorithm is proposed. PSO is adopted to optimize the weights and the topological structure of WNN at the same time, which can overcome the slow convergence rate and easy to drop in the local minimum, and the WNN after optimization is used to the character recognition. The invariant moments of the license plate character image after preprocessing, such as two valued and normalized, are taken as the input of WNN to implement the character recognition. The simulation results show that, compared with WNN, the WNN optimized by PSO algorithm has better performance in character recognition, and has higher recognition ratio.

Methods for evaluation the manufacturability of a vehicle

2020 ◽  
pp. 152-160
Keyword(s):  
Neural Network ◽  
Initial Data ◽  
Comparative Data ◽  
Training Algorithm ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method ◽  
Calculation Results ◽  
Production And Operation

Methods for evaluation the manufacturability of a vehicle in the field of production and operation based on an energy indicator, expert estimates and usage of a neural network are stated. By using the neural network method the manufacturability of a car in a complex and for individual units is considered. The preparation of the initial data at usage a neural network for predicting the manufacturability of a vehicle is shown; the training algorithm and the architecture for calculating the manufacturability of the main units are given. According to the calculation results, comparative data on the manufacturability vehicles of various brands are given.

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