Citation

Tan, Hui Hui and Shahid, Rehan and Mishra, Manish and Lim, Sin Liang (2022) Malaysian Vanity License Plate Recognition Using Convolutional Neural Network. International Journal of Technology, 13 (6). p. 1271. ISSN 2086-9614

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Abstract

Convolutional Neural Network (CNN) is used to train a Malaysian vanity license plate recognition model to recognize vanity license plate available in Malaysia. A transfer learning method is applied in this project to train the model. The type of transfer learning used is finetuning. A modified pretrained ResNet18 network architecture is used to train the Malaysian vanity license plate recognition model. Some hyperparameters such as batch size, learning rate, step size, gamma and momentum are set before training. The optimizer used in this project is SGD (Stochastic Gradient Descent). The available Malaysian vanity license plate images provided by Tapway Sdn Bhd consist of 3 types of Malaysian vanity license plates, which are MALAYSIA, PUTRAJAYA and NORMAL LP (known as Normal License plate). All the images are randomly split into training set (70 % of the total images), validation set (20 % of the total images), and testing set (10 % of the total images) for training. After that, the images are cropped, normalized and transformed into tensors for training. The training is carried out for 70 epochs. Both models trained from original and modified pretrained ResNet18 network architectures are compared and discussed. The accuracy for both models of Malaysian vanity license plate recognition models is 92%. Both training models using the original ResNet18 and modified ResNet18 network architecture approach can be used to train the Malaysian vanity license plate recognition model and obtain similar results.

Item Type:	Article
Uncontrolled Keywords:	Convolutional neural network, Malaysian vanity license plate recognition, Resnet18
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	06 Jan 2023 02:17
Last Modified:	06 Jan 2023 02:17
URII:	http://shdl.mmu.edu.my/id/eprint/10840

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