Publication: Character recognition of Malaysian vehicle license plate with deep convolutional neural networks
| dc.citedby | 9 | |
| dc.contributor.author | How D.N.T. | en_US |
| dc.contributor.author | Sahari K.S.M. | en_US |
| dc.contributor.authorid | 56942483000 | en_US |
| dc.contributor.authorid | 57218170038 | en_US |
| dc.date.accessioned | 2023-05-29T06:37:44Z | |
| dc.date.available | 2023-05-29T06:37:44Z | |
| dc.date.issued | 2017 | |
| dc.description | Classification (of information); Computer networks; Convolution; Intelligent control; Intelligent robots; License plates (automobile); Neural networks; Optical character recognition; Robotics; Smart sensors; Background image; Convolutional networks; Data augmentation; Deep convolutional neural networks; Learning models; Vehicle license; Vehicle license plate recognition; Vehicle license plates; Deep neural networks | en_US |
| dc.description.abstract | This paper presents a vehicle license plate recognition method using deep convolutional neural networks. The focus of this paper is placed on the recognition of segmented characters of vehicle license. The deep convolutional neural network is able to distinguish numbers (0 to 9), alphabets (A to Z) and background image from one another. We show that the neural networks trained on computer fonts and natural images can be used to recognize the characters and non-characters on the vehicle license plates. In our experiments, we compared several models of the deep learning model and measure the performance of each model. We find that deeper models of neural networks yield better recognition results. What also find that the deep convolutional neural network is much more robust at the task of character recognition compared to the deep multilayer perceptron. With approximately equal amount of weights and biases parameters, the deep convolutional neural network outperforms all other models on the same task. Our best model using deep convolutional network, can achieve 95.89% correct classification of real license plate characters when even though the network is only trained on computer fonts (from Chars74K dataset) and natural images (from CIFAR10 dataset). No data augmentation is performed during the training. � 2016 IEEE. | en_US |
| dc.description.nature | Final | en_US |
| dc.identifier.doi | 10.1109/IRIS.2016.8066057 | |
| dc.identifier.epage | 5 | |
| dc.identifier.scopus | 2-s2.0-85050190029 | |
| dc.identifier.spage | 1 | |
| dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050190029&doi=10.1109%2fIRIS.2016.8066057&partnerID=40&md5=3ebd3c3fd1f0c551d121612513e71e59 | |
| dc.identifier.uri | https://irepository.uniten.edu.my/handle/123456789/23094 | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
| dc.source | Scopus | |
| dc.sourcetitle | IRIS 2016 - 2016 IEEE 4th International Symposium on Robotics and Intelligent Sensors: Empowering Robots with Smart Sensors | |
| dc.title | Character recognition of Malaysian vehicle license plate with deep convolutional neural networks | en_US |
| dc.type | Conference Paper | en_US |
| dspace.entity.type | Publication |