License Plate Recognition Algorithm Based on Convolutional Neural Network

2020 ◽  
Author(s):  
Yunxiang Liu ◽  
Xinxin Yuan ◽  
Jinpeng Ren ◽  
Zixuan Lu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Recognition Algorithm ◽  
License Plate ◽  
License Plate Recognition

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.

scholarly journals A design of license plate recognition system using convolutional neural network

2019 ◽  
Vol 9 (3) ◽  
pp. 2196
Author(s):  
P. Marzuki ◽  
A. R. Syafeeza ◽  
Y. C. Wong ◽  
N. A. Hamid ◽  
A. Nur Alisa ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Learning Algorithm ◽  
Recognition System ◽  
Network Size ◽  
License Plate ◽  
License Plate Recognition ◽  
Sobel Operator ◽  
Proposed Model ◽  
Best Parameters

This paper proposes an improved Convolutional Neural Network (CNN) algorithm approach for license plate recognition system. The main contribution of this work is on the methodology to determine the best model for four-layered CNN architecture that has been used as the recognition method. This is achieved by validating the best parameters of the enhanced Stochastic Diagonal Levenberg Marquardt (SDLM) learning algorithm and network size of CNN. Several preprocessing algorithms such as Sobel operator edge detection, morphological operation and connected component analysis have been used to localize the license plate, isolate and segment the characters respectively before feeding the input to CNN. It is found that the proposed model is superior when subjected to multi-scaling and variations of input patterns. As a result, the license plate preprocessing stage achieved 74.7% accuracy and CNN recognition stage achieved 94.6% accuracy.

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