Image Segmentation Based Vehicle Number Identification in Toll Booth

Image Segmentation Based Vehicle Number Identification in Toll Booth

Mr.P.Karthikeyan1, P.Senthur Pandi2, K.Gowtham3, S.Karthikeyan4 1Assistant Professor, 2, 3, 4 UG Students

Department of Electronics and Communication Engineering, Velammal College of Engineering and Technology, Madurai-625009

Abstract- Vehicle number plate recognition is the most interesting and challenging research topic from past few years. It is shown that the number plates are different shape and size and also have different color in different countries. In India the most common vehicle number plate used yellow or white as background and black used as foreground color. In this paper we proposed a system to localization of number plate mainly for the vehicles in West Bengal (India) and segmented the numbers as to identify each number separately. This presents an approach based on simple and efficient morphological operation and sobel edge detection method. We also presents a simple approach to segmented all the letters and numbers used in the number plate. After reducing noise from the input image we try to enhance the contrast of the binarized image using histogram equalization. We mainly concentrate on two steps; one is to locate the number plate and second is to segment all the number and letters to identify each number separately.

Key Word : Vehicle Number Plate Recognition ,Character Recognition

1. INTRODUCTION:

   The automatic number plate recognition systems (ANPR) exist for a long time, but only in the late 90s it became an important application because of the large increase in the number of vehicles. The information extracted from the license plates is mainly used for traffic monitoring, access control, parking, motorway road tolling, and border control, making car logs for parking systems, journey time measurement etc. by the law enforcement agencies. The recognition problem is generally subdivided into 5 parts:

   (1) image acquisition i.e. capturing the image of the license plate (2) pre-processing the image i.e. normalization, adjusting the brightness, skewness and contrast of the image (3) localising the license plate (4) character segmentation i.e. locating and identifying the individual symbol images on the plate, (5) optical character recognition. There may be further refinements over these (like matching the vehicle license number with a particular database to track suspected vehicles etc.) but the basic structure remains the same. A guiding parameter in this regard is country-specific traffic norms and standards. This helps to fine tune the system i.e. number of characters in the license plate, text luminance level (relative index i.e. dark text on light background or light text on dark background) etc. So the problem can then be narrowed down for application in a particular country. For example,

   in India the norm is printing the license plate numbers in black colour on a white background for private vehicles and on a yellow background for commercial vehicles. The general format for the license plate is two letters (for state code) followed by district code, then a four digit code specific to a particular vehicle. In U.S.A no strict guidelines have been set regarding the fonts that can be used for this purpose. Number plate is a pattern with very high variations of contrast. If the number plate is very similar to background it.s difficult to identify the location. Brightness and is changes as light fall changes to it. The morphological operations are used to extract the contrast feature within the plate. The work is divided into several parts:

   1. Input raw image

   2. Image binarization

   3. Reduce noise using mid-filtering method

   4. Enhance contrast using histogram equalizer.

   5. Plate localization

   6. Character segmentation

   licence plate detection/locating plate

   original RGB image

   Grey scale convertion/ binarization

   Noise reduction

   Segmentation of characters in the extracting plate

   Fig.1. Block diagram of proposed approach

   EXPLANATION :

   1. Input raw image :-

   2. Gray scale conversion :-

      From the input RGB image it has to be convert to gray scale and the 8-bit gray value is calculated.

   3. Noise reduction :-

      We used median filtering technique to reduce the paper and salt noise. We have used 3x 3 masks to get eight neighbors of a pixel and their corresponding gray value.

   4. Contrast enhancement using histogram equalization:- Using histogram equalization technique the contrast of each image is being enhanced.

   5. Plate localization:-

      The basic step in recognition of vehicle number plate is to detect the plate size. In general number plates are rectangular in shape. Hence we have to detect the edges of the rectangular plate. Mathematical morphology will be used to detect that region. Using Sobel edge detector we used to high light regions with a high edge magnitude and high edge variance are identified. Depending upon the threshold value edge will be detected from the input image.

   6. Character Segmentation:-

   Matlab toolbox function provides a function called regionprops(). It measures a set of properties for each labeled region in the label matrix. We use boundingbox to measure the properties of the image region. After labeling the connecting components, the region will be extracting from the input image.

   FLOW CHART:

   OUTPUT:

2. RELATED WORK

   S.Roy, A. Choudhury, J. Mukherjee. [1]The proposed a system to localization of number plate mainly for the vehicles in West Bengal (India) and segmented the numbers as to identify each number separately. This paper presents an approach based on simple and efficient morphological operation and sobel edge detection method. He also presents a simple approach to segmented all the letters and numbers used in the number plate. After reducing noise from the input image we try to enhance the contrast of the binarized image using histogram equalization. We mainly concentrate on two steps; one is to locate the number plate and second is to segment all the number and letters to identify each number separately.

   S. Du, M. Shehata, W. Badawy [2] Describe a comprehensive survey on existing (Automatic License Plate Recognition)ALPR Techniques by categorizing them according to the features used in each stage. Comparisons of them in the terms of Pros, Cons, Recognition results, & Processing speeds were addressed. A future forecast for ALPR was also given at the end. The future research of ALPR should concentrate on multistyle plate recognition, video-based ALPR using temporal information, multiplates processing, high definition plate image processing, ambiguous-character recognition.

   P.anishiya, prof. S. Mary joans [3] focused a number plate localization and recognition system for vehicles in Tamilnadu(India) is proposed. This system is developed based on digital images and can be easily applied to commercial car park systems for the use of documenting access of parking services, secure usage of parking houses and also to prevent car theft issues. The proposed algorithm is based on a combination of morphological operation with

   area criteria tests for number plate localization. Segmentation of the plate characters was achieved by the application of edge detectors, labeling and fill hole approach. The character recognition was accomplished with the aid of optical characters by the process of Template matching.

   S. H. Kasaei .,S. M. Kasaei [4] Presented a novel method of identifying and recognizing of Iranian car license plates. Firstly we extracted the plate location, and then we separated the plate characters by segmentation and applied a correlation based template matching scheme for recognition of plate characters. This system is customized for the identification of Iranian license plates. The system is tested over a large number (more than 150) of images, where this algorithm performs well on different types of vehicles including Iranian car and motorcycle plates as well as diverse circumstances. Finally it is proved to be

   %97.3 correct in the extraction of plate region and %94 correct in the segmentation of the characters and %92 in the recognition of the characters. He believe that this system can be redesigned and tested for multinational car license plates in the future time regarding their own attributes.

   D. Jiang,T. M. Mekonnen,T. E. Merkebu,A Gebrehiwot.[5] Discussed paper presents about car plate recognition system.it describes, design algorism and future of implementation. The system has color image inputs of a car and the output has the registration number of that car. The system has three main steps to get the desired information. Those are plate localization, character segmentation and character recognition. First, the number of plate is extracted from the original image, then the characters from it are isolated, and finally each character is recognized. The algorithms were developed using a set of training images. The final program is capable of extracting the desired information in a high percentage of the test images. Z. Xu.,

   H. Zhu.[6] Presented an efficient and robust method of locating license plate is presented. The method makes use of the rich corner information in the plate area and the edge information of license plates. It can deal with more difficult location problems, especially with a license plate existing in a complicated background.

3. CONCLUSION

From review of various paper we conclude that there are different techniques are available for recognition of car number plate. Sobel edge dection method,Automatic license plate recognition, Novel method used for detects edge & fill holes less than 8 pixels only, categoring features in each stage ,identifying & recognizing car license plate.

Therefore at this stage use of improved character segmentation method to reduce effort required for recognizing vehicle license number plate. Try to Calculate improve result as compare to conventional method in turn of time require for convergence.

REFERENCES

1. Sourav Roy, Amitava Choudhury, Joydeep Mukherjee., An Approach towards Detection of Indian Number Plate from VehicleInternational journal of Innovative Technology & Exploring Engineering(IJITEE)Volume-2, Issue-4, March 2013 . International Journal of Scientific and Research Publications, Volume 3, Issue 12, December 2013 3 ISSN 2250-3153 www.ijsrp.org

2. Shan. Du, Mohamed. Shehata, Wael. Badawy ,Automatic License Plate Recognition(ALPR):A State-of the-Art Review IEEE Vol. 23, No.2, June 2013.

3. P.Anishiya, Prof. S. Mary Joans, Number Plate Recognition for Indian Cars Using Morphological Dilation and Erosion with the Aid Of Ocrs. International Conference on Information and Network Technology, Vol.4,2011.

4. S. H. Kasaei .,S. M. Kasaei, " Extraction and Recognition of the Vehicle License Plate for Passing Under Outside Environment." IEEE 2011.

5. D.Jiang,T. M.Mekonnen, T. E.Merkebu,A Gebrehiwot, Car Plate Recognition System. Fifth International Conference on Intelligent Network and Intelligent System 2012.

6. Zhigang Xu, Honglei Zhu, An Efficient Method of Locating Vehicle License Plate,IEEE 2007.

7. Huili Han, Runping Han Method of License plate Location Based on Edge Detection and Color Information IEEE, 978- 1-4577-1701-7/11, 2011, pp1477-1480

8. Ch. Jaya Lakshmi, Dr A.Jhansi Rani, Dr. K. Sri Ramakrishna, M.Lantikiran, V.R. Siddhartha,A novel Approach for Indian License Plate Recognition System, IJAEST, Vol 2 Issue I, 2011, pp 010-014.

9. P.Anishiya, Prof.S.M ary Joans,Number Plate Recognition for Indian Cars Using Morphological Dilation and Erosion with the Aid Of OCRs, IACSIT,Vol.4, 2011, pp 115-119.

10. Phalgun Pandya, Mandeep Singh, Morphology based Approach to Recognize Number Plates in India,