Automatic License Plate Recognition - ijifr license plate recognition (ALPR) is the extraction of...

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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




Prof. Shalaka Shinde 3

Professor




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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

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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

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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.

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4. Flowchart

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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

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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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