A}utomatic License Plate Recoganization System Based on Image Processing Using Lab{VIEW

Automatic License Plate Recoganization System Based

on Image Processing Using LabVIEW

Rachana Chahar

PG Scholar (M.Tech), Computer Science Amity University Rajasthan [email protected]

Dr. Jitendra Kumawat Amity School of Engg. and Tech.

Amity University Rajasthan [email protected]

Himanshu G. Bhavsar Director, VBTech Automation,

Ahmadabad, Gujarat, INDIA [email protected]

Abstract – Automatic License plate recognition (ALPR) system is one kind of an intelligent transport system and is of considerable interest because of its potential applications in highway electronic toll collection and traffic monitoring systems. This allows traffic fines to be automatically generated and sent to the appropriate violator without the need for human intervention. An ALPR system can be located on the side of or above a roadway, at a toll booth, or at another type of entrance way. All ALPR systems follow a basic high level process. The process starts when a sensor detects the presence of a vehicle and signals the system camera to record an image of the passing vehicle. The image is passed on to a computer where software running on the computer extracts the license plate number from the image. License plate numbers can then be recorded in a database with other information such as time vehicle past and speed of vehicle. And finally, chain code concept with different parameter is used for recognition of the characters. The performance of the proposed algorithm has been tested on real images. The Proposed system has been implemented using Vision Assistant & LabVIEW.

Keywords – Image Acquisition, Image Scissoring, Vehicle License Plate Detection (VlPD), Segmentation, Morphology Algorithms, Optical Character Recognition (OCR).

I. I

Automatic Number Plate Recognition (ANPR) is a transportation intelligent control solution that nowadays inseparably combines with several technologies. This technique in software area is chiefly based on concept and principles of machine vision. However, different scientists utilize these techniques along with various intelligent structures and intuitive synthetic methods. In regard to the essence of problem one can classify three factors as main reasons and goals of researches. First is the accuracy which itself is divided into two subclass including accuracy on localization of vehicle license plate and accuracy on recognizing the license plate characters. The second factor is algorithm time complexity which is significant when the science purpose is implementation. Third factor is adaptability as we expect the intelligent agent, the algorithm or the model has the ability to adapt itself with environment to cope with dynamic outdoor conditions therefore without human being intervention the expected

parts including license plate localization, character segmentation and finally character recognition which either has several subparts.

II. I

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ALPR

Selection of camera & CCD size:

Selection of camera is very important concept to acquire high contrast image with maximum clarity, more accuracy and more précised. CCD sensor is the heart of the camera.

Fig.1. Sensor Size

Selection of lens:

A. Lens Mounts: Common Types–C, CS, and F Mounts i). C Mount Lenses work with CS Mount cameras using an adapter.

ii). CS Mount Lenses can NOT work with C Mount or F Mount Cameras.

iii). Mount Lenses work with C Mount cameras using an adapter

B. Lens Format

C. Focal Length

Focal Length = S x (WD / FOV) S= sensor size

WD=working distance FOV=field of view

D. Minimal Working Distance

Selection of Illumination:

Controlled light that can bright up the plate, and allows day and night operation. In most cases the illumination is Infra-Red (IR) which is invisible to the driver. High Performance Infra-Red LED illuminators provide class leading performance.LED’s to deliver excellent night-time pictures with improved optical output, outstanding reliability and even output illumination.

Selection of Image Digitizer:

Image digitizer is an electronic device that captures individual, digital still frames from and analog video signal or digital video stream. It is usually employed as a component of a computer vision system, in which video frames are captured in digital form and then displayed, stored or transmitted in raw or compressed digital form. This has substantially changed in recent years as direct camera connection via USB, Ethernet 1394(fire wire) interfaces has become prevalent.

Selection of Sensor Interface Hardware:

A microcontroller is a highly integrated chip which includes, on one chip, all or most of the parts needed for a controller. The microcontroller could be called a "one-chip solution". It typically includes: CPU (central processing

unit) RAM (Random Access Memory)

EPROM/PROM/ROM (Erasable Programmable Read Only Memory) I/O (input/ output)-serial and parallel timers interrupt controller.

Fig.3. Parallax HomeWork Board

III. I

P

Image processing is a method to convert an image into digital form and perform some operations on it, in order to get an enhanced image or to extract some useful information from it. It is a type of signal dispensation in which input is image, like video frame or photograph and output may be image or characteristics associated with that image. Usually Image Processing system includes treating images as two dimensional signals while applying already set signal processing methods to them. The masked image is passed through the set of processes, to prepare it for the segmentation and OCR phase. We call this preprocessing.

Image acquisition

Image enhancement

Character recoganization segmentation Morphological process

Representation & description Image restorartion

start

stop

Fig.4. Steps of Image Processing

Threshold1 is a Niblack local threshold. Advanced Morphology 1is used to remove border objects. Basic Morphology 1has been used here for dilation. Dilation is used to fill any gaps in the characters and connect them. The Dilated image is then fed to a Gaussian filter, Filter 1, for smoothing and noise reduction prior to the particle filter. Advanced Morphology 2is used here to eliminate small objects, relative to the characters, that are considered as noise.

IV. C

S

Character segmentation applies to both the training and reading procedures. Character segmentation refers to the process of locating and separating each character in the image from the background.

Fig.7. Concepts involved in Character Segmentation 1. Acquired image

2. ROI

3. Character Bounding Rectangle 4. Character

5. Artifact 6. Element

7. Vertical Element Spacing 8. Horizontal Element Spacing 9. Character Spacing

V. C

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The OCR tool extracts unique features from each segmented object in the image and compares them to each character stored in the character set. It then returns the closest character from the character set that best matches the object and returns a nonzero classification score. The character would be accepted if its value is higher than the acceptance level.

Fig.8. OCR Training Interface

Fig.9. LabVIEW code for OCR

VI. C

In this paper, we have developed and implemented a

system for license plate recognition using the NI’s

LabVIEW software. The system is developed under NI environment so that seamless deployment on other commercial platforms is possible. The system starts with

thresholding and other morphological techniques to locate the license plate in the image. A built-in OCR tool for character segmentation and recognition is then used to identify the LP. A database of more than 100 images was used to test the system. A success rate of 94% was achieved under optimum conditions and an overall success rate of 84% with an average processing time of 38 msec/ image. The simplicity of the system and its low computational load makes it very attractive for real-time applications. Future work considers improving the localization technique, and the extension of the system to other GCC license plates.

A

It is indeed a great pleasure and matter of immense satisfaction for us to express our deep sense profound gratitude towards all the people who have helped, inspired us in our project work. First I would like to give our gratitude to Dr. Jitendra Kumawat (Assistant Professor, ASET) for the effort taken by him right from the selection of the project to its completion. He spent his precious time whenever I was in need of guidance.

Moreover I would like to thank Himanshu G. Bhavsar (Director VBTech Automation, Ahmadabad, Gujarat, INDIA), who was always there whenever we needed any support and was a constant source of inspiration for accomplishment of this project.

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Rachana Chahar

received the B.Tech. degree in Computer Science and Engineering from BMIT, Rajasthan Technical University, Jaipur, India, in 2012, the full time M.Tech. Degree in Computer Science and Engineering from Amity University Rajasthan, Jaipur, India, in 2014. Her research interests include Image Acquisition; Image Scissoring; Vehicle license plate detection (VLPD); Segmentation; morphology algorithms; optical character recognition (OCR).

Email: [email protected]

Jitendra Kumawat

received the B.E. degree in Computer Science and Engineering from Sri Balaji College of Engineering and Technology, Jaipur, University of Rajasthan, India, in 2004, the M.Tech. degree in Computer Science and Engineering fro Sri Balaji College of Engineering and Technology, Jaipur, Rajasthan Technical University, Kota, India, in 2010 and the Ph.D. degree in Computer Science and Technology from the CMJ University, Meghalaya, India, in 2013. He is currently Sr. Lecturer and Program Coordinator in Computer Science and Engineering Department of Amity University Rajasthan, Jaipur, India, Since July 2011. His research interests include Wireless cellular networks, Communication and Networks and Security. He has about 10 years of teaching experience, since 2004. He has served as lecturer, Sr. Lecturer and the life Member of IET, India.

Email: [email protected]

Himanshu G. Bhavsar

received his degree in Bachelor of Science With electronics From Gujarat University, Gujarat, India. He is Director of VBTech Automation, Ahmadabad, and Gujarat, India. He is having more than 25 years of experience in Industrial Automation, Laboratory Automation, Process Control, Research & Development and LabVIEW based automation. During his professional carrier he has worked for multinational engineering, IT & automation companies as Technical Director. He has started his own company VBTech Automation in the year 2003 to provide exclusive IT enabled Automation solutions on National Instruments LabVIEW platform. He is one of the system integrator of NI to provide software /hardware based automation solutions.