Title
Categorizing License Plates Using Convolutional Neural Network with Residual Learning
Abstract
Like other countries, the Philippines uses various license plate standards wherein some purely text while some are hybrid graphic-text plates. And to harness its generalizability, this study developed a classification algorithm utilized as a pre-processing scheme for the multi-standard license plate. With an input image captured at a different perspective, it was feed into the neural network and classify as Rizal monument series (2001 base and 2003 base), 2014 series and conduction sticker for new vehicles. In total, there are 303 different images captured for this study. Around 100 conduction sticker images, 103 Rizal Monument images, 100 black and white images. Furthermore, this study focused on using transfer learning technique, wherein a trained network utilized, then only the last layer was reset and retrained on the new dataset. To measure the performance of the classification model and optimized it cross-entropy and stochastic gradient descent was employed respectively at a learning rate of 0.001 and reduced by 10 for every seven (7) epochs. The progression of accuracy results in increasing the epochs, and for the 25 epochs, the training completed in 4 minutes and 7 seconds with the best validation accuracy of 82.61%.
Year
DOI
Venue
2019
10.1109/ACIRS.2019.8935997
2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS)
Keywords
Field
DocType
license plate classification,convolutional neural network,residual learning,transfer learning
Generalizability theory,Residual,Stochastic gradient descent,Pattern recognition,Convolutional neural network,Computer science,Transfer of learning,Artificial intelligence,Artificial neural network,License
Conference
ISBN
Citations 
PageRank 
978-1-7281-2230-4
0
0.34
References 
Authors
0
8