An Algorithm for License Plate Recognition Applied to Intelligent Transportation System

2011 ◽  
Vol 12 (3) ◽  
pp. 830-845 ◽  
Author(s):  
Ying Wen ◽  
Yue Lu ◽  
Jingqi Yan ◽  
Zhenyu Zhou ◽  
Karen M. von Deneen ◽  
...  
2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Sahid Bismantoko ◽  
M. Rosyidi ◽  
Umi Chasanah ◽  
Asep Haryono ◽  
Tri Widodo

Automatic License Plate Recognition is related to the Intelligent Transportation System (ITS) that supports the road's e-law enforcement system. In the case of the Indonesian license plate, with various colour rules for font and background, and sometimes vehicle owners modify their license plate font format, this is a challenge in the image processing approach. This research utilizes pre-trained of AlexNet, VGGNet, and ResNet to determine the optimum model of Indonesian character license plate recognition. Three pre-trained approaches in CNN-based detection for reducing time for a build if model from scratch. The experiment shows that using the pre-trained ResNet model gives a better result than another two approaches. The optimum results were obtained at epoch 50 with an accuracy of 99.9% and computation time of 26 minutes. This experiment results fulfil the goal of this research. Keywords : ALPR; ITS; CNN; AlexNet; VGGNet; ResNet


Author(s):  
John Paolo D. Dalida ◽  
A-Jay N. Galiza ◽  
Aleck Gene O. Godoy ◽  
Masaru Q. Nakaegawa ◽  
Jean Louise M. Vallester ◽  
...  

2012 ◽  
Vol 485 ◽  
pp. 592-595
Author(s):  
Sheng Bing Che ◽  
Jin Kai Luo

With the development and progress of the economic and technology, transportation has become more and more important in human’s usual life. Intelligent transportation system can be used in many areas. Images in RGB color space are very sensitive to the environment light intensity, and what’s more, there may be some pollution appeared on the plate and so on, these made it difficult to locate the plate area. In this paper, we proposed some new algorithms. In the first stage, car image preprocess, a new algorithm named color division was proposed. Experiments show that, influences from the environment as well as some inevitably differences of colors in the plate into standard color, so the performance of whole system was improved. In the period of license location, a new algorithm based on license color pairs was proposed, and it has good performance after experiments.


Author(s):  
Joy Iong Zong Chen

Because of the development of highways as well as the increased number of vehicles usage, much attention is required on to develop an efficient and safe intelligent transportation system. The aspect of identifying specific objects present in an image is an important criteria in areas like digital image processing and computer vision. Because of the different formats, colours, shapes, viewpoints and non-uniform illumination environment of license plates, recognising the same proves to be a tasking issue. In this paper, we present a vehicle license plate recognition model using convolutional neural network (CNN) and K-means clustering based segmentation. This methodology works on three major steps such as detection and segmentation using K-means clustering and recognition of the number in the license plate using CNN model. We have also used location and detection algorithms to improve the accuracy of detection. The experimental investigation is carried out using datasets and the observed simulation results prove that the proposed mode is more effective than the other methodologies introduced so far.


Sign in / Sign up

Export Citation Format

Share Document