The week's pick
Reseach Article
Design of a Recognition System Automatic Vehicle License Plate through a Convolution Neural Network
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 177 - Number 3 |
| Year of Publication: 2017 |
| Authors: P. Rajendra, K. Sudheer Kumar, Rahul Boadh |
10.5120/ijca2017915703
|
P. Rajendra, K. Sudheer Kumar, Rahul Boadh . Design of a Recognition System Automatic Vehicle License Plate through a Convolution Neural Network. International Journal of Computer Applications. 177, 3 ( Nov 2017), 47-54. DOI=10.5120/ijca2017915703
Abstract
The present work is a study on the practical application of Learning process (Deep Learning) in the development of a system of Automatic recognition of vehicle license plates. These systems commonly referred to as ALPR (Automatic License Plate Recognition) - are able to recognize the content of vehicles from the images captured by a camera. The system proposed in this work is based on an image classifier developed through supervised learning techniques with convolution neural network. These networks are one of the most profound learning architectures and are specifically designed to solve artificial vision, such as pattern recognition and classification of images. This paper also examines basic processing techniques and Image segmentation - such as smoothing filters, contour detection - necessary for the proposed system to be able to extract the contents of the license plates for further analysis and classification. This paper demonstrates the feasibility of an ALPR system based on a convolution neural network, noting the critical importance it has to design a network architecture and training data set appropriate to the problem to be solved.
References
- ImageNet Project. http://image-net.org - March 2017.
- ImageNet ILSVRC2015.http://image-net.org/challenges/LSVRC/2015/results - March 2017.
- Chars74k dataset. TE Campos, BR Babu, M. Varma. http://www.ee.surrey.ac.uk/CVSSP/demos/chars74k/ - March 2017.
- Report 425/2006. Spanish Data Protection Agency (AGPD).
- Platesmania License Plate Generator. http://platesmania.com/es/informer - April 2017
- Acme License Maker. http://acme.com/licensemaker - April 2017
- ImageNet Classification With Deep Convolutional Neural Networks. Krizhevsky et al.http://papers.nips.cc/paper/4824-imagenet classification- with-deep-convolutional-neural-networks.pdf March 2017
- Gradient-Based Learning Applied to Document Recognition. LeCun et al.http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf - March 2017
- Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis.Simardetal. http://research.microsoft.com/pubs/68920/icdar03.pdf - April 2017
- Fundamentals of Deep Learning. Nikhil Buduma. O'Reilly. http://shop.oreilly.com/product/0636920039709.do - April 2017
- Deep Learning Book. Ian Goodfellow, Yoshua Bengio,Aaron Courville. MIT Press.http://www.deeplearningbook.org - March 2017
- Dropout: A Simple Way to Prevent Neural Networks from Over fitting. Srivastava et al. https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf - May 2017
- Subba Rao, A et al. "Numerical Study of Non-Newtonian Polymeric Boundary Layer Flow And Heat Transfer from a Permeable Horizontal Isothermal Cylinder." Frontiers in Heat and Mass Transfer FHMT) 9, no. 1 (2017).
- Rajendra, P., et al. "Potential Distribution in a Nano Channel of an Electrolytic Solution by Finite Element Method." I J C T A, 9(32), 2016, pp. 145-151
- Padidhapu, Rajendra, Shahnaz Bathul, and V. Brahmajirao. "Least Square and Gauss Jordan Methods applied to fit the Dielectric data Vs. Concentration of Ionic Liquids." International Journal of Science and Technology 2.1 (2013).
- Boadh, Rahul, et al. "numerical simulation of boundary layer flow parameters by using wrf-arw model over a tropical region.", Jr. of Industrial Pollution Control 33(1) (2017) pp 1148-1154.
Index Terms
Keywords