International Journal of Scientific & Engineering Research, Volume 6, Issue 1, January-2015 594
ISSN 2229-5518
License Plate Recognition using Color based
Segmentation and Neural Networks
1Gurjinder Pal Singh and 2Navneet Bawa
1Department of CSE, DIET, Kharar, Punjab, INDIA
2Department of CSE, ACET, Amritsar, Punjab, INDIA
—————————— ——————————
Number-plate recognition is a mass surveillance method that uses Hopfield Neural Network character recognition on images to read the license plates on vehicles. They can use existing closed-circuit television or road-rule enforcement cameras, or ones specifically designed for the task. They are used by various police forces and as a method of electronic toll collection on pay- per-use roads and cataloging the movements of traffic or individuals.
NPR can be used to store the images captured by the cameras as well as the text from the license plate, with some configurable to store a photograph of the driver. Systems commonly use infrared lighting to allow the camera to take the picture at any time of the day. ANPR technology tends to be region-specific, language specific, owing to plate variation from place to place.
Vehicle identification is a research area where image processing methods are used to identify vehicles by detecting and identifying the license plate numbers. Typical vehicle identification systems consist of three main stages. They are the identification and tracking of vehicles through motion, locating the license plate, and
accurately identifying the numbers in the license plate.
Although many intensive research studies have been conducted in other countries in the area of automatic vehicle identification, to our knowledge, there is virtually no research studies conducted on some parts of the work.
algorithm for clear image extraction from blurred image. From this improved image, licence plate region is recognized and plate is extracted .The character segmentation is performed on extracted licence plate and every single character is recognized by using two Neural network techniques: Back propagation artificial neural network(BP ANN), Learning vector quantization neural network(LVQ NN). [2]
IJSER © 2015 http://www.ijser.org
International Journal of Scientific & Engineering Research, Volume 6, Issue 1, January-2015 595
ISSN 2229-5518
The vehicle’s image captured on road is noisy and blur
and therefore are required for noise and illumination
variations compensation during the task of identification of texts on the number plate. It is important to preserve the edges so that the number plate area is recovered and text identification algorithm may be confined to the bounding box.
In the existing system, the problem of zoom in or zoom out i.e. the size of the texts on RC plate due to image acquisition arises very frequently. Further, the system suffers from the problem of orientation of the text on the plate due to camera movement or vehicle side image capturing. If the systems are enriched with the information of the vehicle for the last 24 hours where the vehicle has been passed by, that may be of great interest while tracking the theft vehicle. This kind of information bank has been provisioned in the presented approach.
Normally the number plate bears a white o yellow color as base color for private and commercial vehicles respectively. This information is used to extract the number plate from the ear body of the vehicle. This is done by segmenting the image based on color. K-means clustering is used for color based segmentation.
A neural network is trained for identification of character segmented form the number plate. The neural network approach provides the flexibility of identifying the characters written in different font style. If the neural network is trained for different font style, then little fluctuation to some extent may absorbed by the neural network algorithm and correct identification of text could be carried out.
Another important part regarding the textual information on number plate, the text style or font is not many times uniform on all plates. Therefore, the vehicle’s no. plate identification system should be capable of identifying the texts written in any style and at any angular position. Even the size should not bother much.
IJSER © 2015 http://www.ijser.org
International Journal of Scientific & Engineering Research, Volume 6, Issue 1, January-2015 596
ISSN 2229-5518
The vehicles in India sometimes bare extra textual regions, such as owner’s name, symbols, popular sayings and advertisement boards in addition to license plate. Situation insists for accurate discrimination of text class and fine aspect ratio analysis. In addition to this additional care taken up in the proposed work is to extract license plate of motorcycle (size of plate is small and double row plate), car (single as well as double row type), transport system such as bus, truck, (dirty plates) as well as multiple license plates present in an image frame under consideration.
Block Diagram of the Proposed System
The presented studied work in identification of text from the number plate suffers from the draw back of same color of vehicles and number plate i.e. if the vehicle rear side is also of yellow in color and number plate is already in yellow. The main problem is to extract or localize the no. plate form the image irrespective of vehicle base color. This may be covered by using the derivative of color intensity on boundaries.
1. Jitendra Sharm , Amit Mishra,Khushboo Saxena, Shiv
Kumar,” A Hybrid Technique for License Plate Recognition
Based on Feature Selection of Wavelet Transform and Artificial Neural Network”, 2014 International Conference on Reliability, Optimization and InformationTechnology- ICROIT,978-1-4799-2995-5/14/$31.00©2014 IEEE.
2. DIPALEE A. KOLTE, MARUTI B. LIMKAR2 & SANJAY M. HUNDIWALE3,” LICENSE PLATE CHARACTER RECOGNITION SYSTEM FROM A BLURRED IMAGE BY USING NEURAL NETWORK”, International Journal of Electrical and Electronics Engineering Research (IJEEER) ISSN(P): 2250-155X; ISSN(E): 2278-943X Vol. 4, Issue 2, Apr 2014.
3. Seyed Hamidreza Mohades Kasaei Seyed, Mohammadreza Mohades Kasaei,”EXTRACTION AND RECOGNITION OF THE VEHICLE LICENSE PLATE FOR PASSING UNDER OUTSIDE ENVIRONMENT “,2011 European Intelligence and Security Informatics Conference.
4. Rajesh Kannan Megalingam, Prasanth Krishna, Pratheesh somarajan, Vishnu A Pillai “Extraction of License Plate Region in Automatic License Plate Recognition “,201O International Conference on Mechanical and Electrical Technology (ICMET 2010) .
5. Shyang-Lih Chang, Li-Shien Chen, Yun-Chung Chung, and Sei-Wan Chen,”Automatic License Plate Recognition”,IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 5, NO. 1, MARCH 2004
6. Zhihong Zhao, Shaopu Yang, Xinna Ma,” Chinese License
Plate Recognition Us ing a Convolutional Neural Network”,
2008 IEEE Pacific-Asia Workshop on Computational
Intelligence and Industrial Application.
7. Al Hussain AKOUM CREAMlIRFA, BassamDAYA Lebanese University ,“Automatic System Recognition of Lebanese License Plates “, 978-1-4244-6439-5/10/$26.00 ©2010 IEEE.
8. Nabeel Younus Khan , Ali Shariq Imran “Distance and Color
Invariant Automatic License Plate Recognition System”, 1-
4244-1494-6/07/$25.00 C 2007 IEEE.
9. W. K. I. L. Wanniarachchi1, D. U. J. Sonnadara2 and M. K.
Jayananda3, “License Plate Identification Based on Image
Processing Techniques”, 1-4244-1152-/07/$25.00 ©2007 IEEE
10. Serkan Ozbay, and Ergun Ercelebi, “Automatic Vehicle Identification by Plate Recognition”, World Academy of Science, Engineering and Technology 9 2005
11. Nahian Alam Siddique1, Asif Iqbal2, Fahim Mahmud, Md.
Saifur Rahman. “Development of an Automatic Vehicle License Plate Detection and Recognition System for Bangladesh”, 978-1-4673-1154-0/12/$31.00 ©2012 IEEE
12. Huang Wenjie, “Automatic Vehicle License Plate Recognition
System Used in Expressway Toll Collection”, 978-1-4244-
5540-9/10/$26.00 ©2010 IEEE
13. Shokri Gendy, Clifton L. Smith, and Stefan Lachowicz, “Automatic Car Registration Plate Recognition Using Fast Hough Transform”, 0-7803-391 3-4-9/97/$4.00 01 997 IEEE
14. Sang Kyoon Kim, Due Wook Kim and Hang Joon Kim, “A
RECOGNITION OF VEHICLE LICENSE PLATE USING A
IJSER © 2015 http://www.ijser.org
International Journal of Scientific & Engineering Research, Volume 6, Issue 1, January-2015 597
ISSN 2229-5518
GENETIC ALGORITHM BASED SEGMENTATION”, 0- 7803-3258-x/96/$5.00 0 1996 IEEE
The 1author is pursuing his M.Tech. (CSE) thesis work in image processing from ACET, Amritsar (Punjab) India. His field of interest is in image processing based application development.
IJSER © 2015 http://www.ijser.org