Character Recognition of License Plate Number Using Convolutional Neural Network

2011 ◽  
pp. 45-55 ◽  
Author(s):  
Syafeeza Ahmad Radzi ◽  
Mohamed Khalil-Hani
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Character Recognition ◽  
License Plate ◽  
Plate Number

BP-Neural Network for Plate Number Recognition

2016 ◽  
Vol 8 (3) ◽  
pp. 34-45 ◽  
Author(s):  
Jia Wang ◽  
Wei Qi Yan
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Back Propagation ◽  
License Plate ◽  
License Plate Recognition ◽  
Traffic Surveillance ◽  
Plate Number ◽  
Number Recognition ◽  
Traffic Systems ◽  
Artificial Neural Network Ann

The License Plate Recognition (LPR) as one crucial part of intelligent traffic systems has been broadly investigated since the boosting of computer vision techniques. The motivation of this paper is to probe in plate number recognition which is an important part of traffic surveillance events. In this paper, locating the number plate is based on edge detection and recognizing the plate numbers is worked on Back-Propagation (BP) Artificial Neural Network (ANN). Furthermore, the authors introduce the system implementation and take advantage of the well-known Matlab platform to delve how to accurately recognize plate numbers. There are 80 samples adopted to test and verify the proposed plate number recognition method. The experimental results demonstrate that the accuracy of the authors' character recognition is above 70%.

BP-Neural Network for Plate Number Recognition

2020 ◽  
pp. 1189-1199
Author(s):  
Jia Wang ◽  
Wei Qi Yan
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Back Propagation ◽  
License Plate ◽  
Traffic Surveillance ◽  
Bp Artificial Neural Network ◽  
Plate Number ◽  
Number Recognition ◽  
Traffic Systems ◽  
Artificial Neural Network Ann

The License Plate Recognition (LPR) as one crucial part of intelligent traffic systems has been broadly investigated since the boosting of computer vision techniques. The motivation of this paper is to probe in plate number recognition which is an important part of traffic surveillance events. In this paper, locating the number plate is based on edge detection and recognizing the plate numbers is worked on Back-Propagation (BP) Artificial Neural Network (ANN). Furthermore, the authors introduce the system implementation and take advantage of the well-known Matlab platform to delve how to accurately recognize plate numbers. There are 80 samples adopted to test and verify the proposed plate number recognition method. The experimental results demonstrate that the accuracy of the authors' character recognition is above 70%.

scholarly journals BengaliNet: A Low-Cost Novel Convolutional Neural Network for Bengali Handwritten Characters Recognition

2021 ◽  
Vol 11 (15) ◽  
pp. 6845
Author(s):  
Abu Sayeed ◽  
Jungpil Shin ◽  
Md. Al Mehedi Hasan ◽  
Azmain Yakin Srizon ◽  
Md. Mehedi Hasan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Character Recognition ◽  
High Performance ◽  
Low Cost ◽  
Traditional Learning ◽  
Neural Network Architecture ◽  
Handwritten Character Recognition ◽  
Handwritten Character

As it is the seventh most-spoken language and fifth most-spoken native language in the world, the domain of Bengali handwritten character recognition has fascinated researchers for decades. Although other popular languages i.e., English, Chinese, Hindi, Spanish, etc. have received many contributions in the area of handwritten character recognition, Bengali has not received many noteworthy contributions in this domain because of the complex curvatures and similar writing fashions of Bengali characters. Previously, studies were conducted by using different approaches based on traditional learning, and deep learning. In this research, we proposed a low-cost novel convolutional neural network architecture for the recognition of Bengali characters with only 2.24 to 2.43 million parameters based on the number of output classes. We considered 8 different formations of CMATERdb datasets based on previous studies for the training phase. With experimental analysis, we showed that our proposed system outperformed previous works by a noteworthy margin for all 8 datasets. Moreover, we tested our trained models on other available Bengali characters datasets such as Ekush, BanglaLekha, and NumtaDB datasets. Our proposed architecture achieved 96–99% overall accuracies for these datasets as well. We believe our contributions will be beneficial for developing an automated high-performance recognition tool for Bengali handwritten characters.

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