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Copyright © IJIFR 2015
Reviewed Paper
International Journal of Informative & Futuristic Research ISSN (Online): 2347-1697
Volume 2 Issue 7 March 2015
Abstract
Automatic license plate recognition (ALPR) is the extraction of vehicle license plate information from an image or a sequence of images. The ALPR uses either a color, black and white, or infrared camera to take images. The extracted information can be used with or without a database in many applications, such as electronic payment systems (toll payment parking fee payment), and freeway and arterial monitoring systems for traffic surveillance. ALPR as a real life application has to quickly and successfully process license plates under different environmental conditions, such as indoors, outdoors, day or night time. The quality of the acquired images is a major factor in the success of the ALPR. It should also be generalized to process license plates from different nations, provinces, or states. These plates usually contain different colors, are written in different languages, and use different fonts; some plates may have a single color background and others have background images. The license plates can be partially occluded by dirt, lighting, and towing accessories on the car. In this paper, we present a comprehensive review of the state-of-the-art techniques for ALPR. We categorize different ALPR techniques according to the features they used for each stage, and compare them in terms of pros, cons, recognition accuracy, and processing speed. Future forecasts of ALPR are given at the end.
Automatic License Plate Recognition
Paper ID IJIFR/ V2/ E7/ 010 Page No. 2217-2223 Subject Area Electronics &
Telecommunication
Key Words Pre-processing, Character recognition. Character segmentation, Number plate
localization
Kajal A. Mandhare 1
BE Student
Department of Electronics & Telecommunication Engg.
Parvatibai Genba Moze College Of Engineering,
Wagholi, Pune -Maharashtra
Pornima P. Bagate 2
BE Student
Department of Electronics & Telecommunication Engg.
Parvatibai Genba Moze College Of Engineering,
Wagholi, Pune -Maharashtra
Prof. Shalaka Shinde 3
Professor
Department of Electronics & Telecommunication Engg.
Parvatibai Genba Moze College Of Engineering,
Wagholi, Pune -Maharashtra
2218
ISSN (Online): 2347-1697 International Journal of Informative & Futuristic Research (IJIFR)
Volume - 2, Issue - 7, March 2015 19th Edition, Page No: 2217-2223
Kajal A. Mandhare , Pornima P. Bagate , Pankaj Patil:: Automatic License Plate Recognition
1. Introduction
Automatic license plate recognition (ALPR) is the extraction of vehicle license plate information
from an image or a sequence of images. The extracted information can be used with or without a
database in many applications, such as electronic payment systems (toll payment, parking fee
payment), and freeway and arterial monitoring systems for traffic surveillance. The LPR uses either
a color, black and white, or infrared camera to take images. The quality of the acquired images is a
major factor in the success of the ALPR. ALPR as a real life application has to quickly and
successfully process license plates under different environmental conditions, such as indoors,
outdoors, day or night time. It should also be generalized to process license plates from different
nations, provinces, or states. These plates usually contain different colors, are written in different
languages, and use different fonts; some plates may have a single color background and others have
background images. The license plates can be partially occluded by dirt, lighting, and towing
accessories on the car. In this paper, we present a comprehend severe view of the state-of-the-art
techniques for ALPR. We categorize different ALPR techniques according to the features they used
for each stage, and compare them in terms of pros, cons, recognition accuracy, and processing speed.
Future forecasts of ALPR are given at the end.
2. Problem Prevailing
Existing System Needs:
• Large Amount of Database
• More Training Sets
• Large Memory to Run the System
• Sensitive to Distance and Angle between Camera and License Plate
3. Our Proposed System:
Minimize Database Requirement
Less Training Sets
Minimize Memory Requirement
Can Be Able To Recognize Image From Any Distance And Angle
4. Block Diagram
Figure 3.1: Basic Block Diagram of ALPR System
2219
ISSN (Online): 2347-1697 International Journal of Informative & Futuristic Research (IJIFR)
Volume - 2, Issue - 7, March 2015 19th Edition, Page No: 2217-2223
Kajal A. Mandhare , Pornima P. Bagate , Pankaj Patil:: Automatic License Plate Recognition
Block Diagram Explanation
Image acquisition:
In image acquisition explained that from where images are acquire. Image can be
input to the system by different methods by analog camera, or by digital cameras, but nowadays
digital technology has their advantages so better input method is by digital cameras or by direct
digital photos.
Figure 3.2: Captured image
License Plate Extraction:
By whole capturing image we having license plate covered by background
of vehicle body, so by this step only plate are is extracted from whole body. our task now is to
identify the region containing the license plate. In this experiment, two features are defined and
extracted in order to decide if a candidate region contains a license plate or not , these features are
Figure 3.3: license plate area extraction
Character segmentation:
By this step characters on license plate are segmented and identify. This step is
the most important step in license plate recognition because all further steps rely on it. This is the
second major part of the License Plate detection algorithm. There are many factors that cause the
character segmentation task difficult, such as image noise, plate frame, rivet, space mark, plate
rotation and illumination variance. We here propose the algorithm that is quite robust and gives
2220
ISSN (Online): 2347-1697 International Journal of Informative & Futuristic Research (IJIFR)
Volume - 2, Issue - 7, March 2015 19th Edition, Page No: 2217-2223
Kajal A. Mandhare , Pornima P. Bagate , Pankaj Patil:: Automatic License Plate Recognition
significantly good results on images having the above mentioned problems. for the segmentation
pre-processing is required by conversion to gray scale and binarization. Different algorithms are
used for segmentation which are explained further later in literature review. segmented license plate
example is given in figure.
Figure 3.4: Segmented license plate
License plate number recognize:
By number plate extraction step final result is founded. Consider
figure as an final extracted license plates
.
Figure 3.5: License plate number recognize
The variations of the plate types or environments cause challenges in the detection and recognition
of license plates. They are summarized as follows:
i. Location: Plates exist in different locations of an image.
ii. Quantity: An image may contain no or many plates.
iii. Size: Plates may have different sizes due to the camera distance and the zoom factor.
iv. Colour: Plates may have various characters and background colours due to different plate
types or capturing devices.
v. Font: Plates of different nations may be written in different fonts and language.
vi. Occlusion: Plates may be obscured by dirt.
vii. Inclination: Plates may be tilted.
viii. Other: In addition to characters, a plate may contain frames and screws.
2221
ISSN (Online): 2347-1697 International Journal of Informative & Futuristic Research (IJIFR)
Volume - 2, Issue - 7, March 2015 19th Edition, Page No: 2217-2223
Kajal A. Mandhare , Pornima P. Bagate , Pankaj Patil:: Automatic License Plate Recognition
4. Flowchart
2222
ISSN (Online): 2347-1697 International Journal of Informative & Futuristic Research (IJIFR)
Volume - 2, Issue - 7, March 2015 19th Edition, Page No: 2217-2223
Kajal A. Mandhare , Pornima P. Bagate , Pankaj Patil:: Automatic License Plate Recognition
5. Result
a) Input Image b) Grayscale Image
c)Unwanted Lines Elimination Algo d)Vertical Egde Detection Algo
e) Candidate Region Extraction f) Output
g) Final Output
2223
ISSN (Online): 2347-1697 International Journal of Informative & Futuristic Research (IJIFR)
Volume - 2, Issue - 7, March 2015 19th Edition, Page No: 2217-2223
Kajal A. Mandhare , Pornima P. Bagate , Pankaj Patil:: Automatic License Plate Recognition
6. Advantages , Disadvantages & Applications
Disadvantages:
Needs high resolution digital camera
Sensitive to environmental conditions
Advantages:
Vehicle access
Traffic control
Stopping vehicle related crimes
Searching for missing or wanted persons
Recovering stolen cars
Applications:
Parking
Access Control
Motorway Road Tolling
Border Control
Journey Time Measurement
7. Conclusion & Future Scope
Hence we have seen Automatic license plate recognition (ALPR) system. Is a extraction of vehicle
license plate information from an image or a sequence of images. Secure transportation will occur in
this system. The extracted information can be used with or without a database in many applications.
It is also monitor traffic surveillance. We can also use this system in electronic registration of
vehicle.
References
[1] Muhammad Sarfraz, Mohammed Jameel Ahmed, and Syed A. Ghazi ,“Saudi arebian licence plate
recognition system”, International Conference on Geometric Modeling and Graphics (GMAG’03), 2003.
[2] Serkan Ozbay, and Ergun Ercelebi,” Automatic Vehicle Identification by Plate Recognition”, Processing of
world academy of science engineering and technology vol9, ISSN 1307-6884, november 2005.
[3] Humayun Karim Sulehria, Ye Zhang, Danish Irfan, Atif Karim Sulehria,” Vehicle Number Plate
Recognition Using Mathematical Morphology and Neural Networks”, WSEAS TRANSACTIONS on
COMPUTERS, Volume 7,ISSN: 1109-2750, Issue 6, June 2008.
[4] Zhen-Xue Chen, Cheng-Yun Liu, Fa-Liang Chang, and Guo-You Wang,” Automatic License-Plate
Location and Recognition Based on Feature Salience”, IEEE Transaction on vehicle technology, VOL. 58,
NO. 7, september 2009.
[5] Dr. P.K.Suri, Dr. Ekta Walia, Er. Amit Verma,” Vehicle Number Plate Detection using Sobel Edge
Detection Technique”, International Journal of Computer Science and Technology, ISSN : 2229 – 4333, IJCST
Vol. 1, Issue 2, December 2010.
[6] Kumar Parasuraman, Member, IEEE and P.Vasantha Kumar, “ An Efficient Method for Indian Vehicle
License Plate Extraction and Character Segmentation”, IEEE International Conference on Computational
Intelligence and Computing Research,2010.
[7] Muhammad H Dashtban, Zahra Dashtban, Hassan Bevrani, “ A Novel Approach for Vehicle License Plate
Localization and Recognition”, International Journal of Computer Applications (0975 – 8887), Volume 26–
No.11, July 2011.
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