3.ijaest-vol-no-6-issue-no-1-a-novel-approach-for-indian-license-plate-recognition-system-010-014

A Novel Approach for Indian License Plate

Recognition System

Ch.Jaya Lakshmi, Dr.A.Jhansi Rani Dr.K.Sri Ramakrishna M.KantiKiran V.R.Siddhartha V.R.Siddhartha V.R.Siddhartha V.R.Siddhartha Engineering College Engineering College Engineering College Engineering College Vijayawada Vijayawada Vijayawada Vijayawada [email protected] [email protected] [email protected] [email protected]

Abstract - License Plate Recognition System (LPRS) is one of

the most important part of the Intelligent Transportation

System (ITS). License plate automatic recognition has been

extensively applied in the Intellectual traffic system and

Public transit security system. The main objective is to design

an efficient automatic authorized vehicle identification system

by using the vehicle number plate. Location of the license

plate is performed using its inherent texture characteristics

and wavelets. Character segmentation is done using

Connected Component Analysis. Character Recognition of the

vehicle license plate based on template matching. This system

is implemented using MATLAB. The proposed system mainly

applicable to Indian License Plates.

Keywords : LPR, Wavelets, Location, Segmentation, Character

Recognition.

I.INTRODUCTION The Automatic Number Plate Recognition (ANPR) was invented in 1976 at the Police Scientific Development Branch in the UK. License Plate Recognition system (LPRS) is similar to ANPR. However, it gained much interest during the last decade along with the improvement of digital camera and the increase in computational capacity. It is simply the ability to automatically extract and recognition of vehicle number plate’s characters from an image. In essence it consists of a camera or frame grabber that has the capability to grab an image, find the location of the number in the image and then extract the characters for character recognition tool to translate the pixels into numerically readable character. It can also be used to detect and prevent a wide range of criminal activities and for security control of a highly restricted areas like military zones or area around top government offices. The system is computationally inexpensive compare to the other ANPR systems. The ANPR system

works in three steps, the first step is the detection and capturing a vehicle image, the second steps is the detection and extraction of number plate in an image. The third section use image segmentation technique to get individual character and optical character recognition (OCR) to recognize the individual character with the help of database stored for each and every alphanumeric character. In India, Vehicles used for domestic purpose contains the license plate with white color as its background and black characters as its foreground whereas vehicles used for commercial purpose contains the license plate with yellow color as its background and black characters as its foreground.

II. Related work

There are several common algorithms to locate the license plate. Searching algorithms mainly rely on color information and special signs. Widely used procedures that are solely based on image processing are as Hough transform, Top-Hat and Bottom-Hat filtering (highlights the black-white transitions) [3] Binary morphology algorithm (for example: classical Otsu method) [5]Edge finding methods (Sobel, Laplacian, Roberts, Prewitt, Canny operators) [4] [5] [2] Procedures based on the color of the background and characters. Region-growing algorithm (RGA): By using a recursive region-growing algorithm, the dark regions (license plate symbols) surrounded by light areas (background of the license plate) can then be classified. Each region has a unique position and dimensions. [1]Checking: color, size, ratio. Presently, there are several common algorithms for the segmentation of license plate characters, such as direct segmentation, template matching, projection and cluster analysis. (1) In the direct segmentation algorithm[6], the license plate characters are segmented directly according to the prior knowledge of the width of characters and spaces between characters. This algorithm is simple and rapid. But the left and right side of characters region must be located

Ch.Jaya Lakshmi et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 6, Issue No. 1, 010 - 014

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accurately before using it. So it is reliable to the effect of license plate location. (2) In the template matching algorithm [7], a template is used to scan the image of license plate to find the maximum difference value of the number of white points between the region of character and the region of space between characters. This algorithm can avoid falsely segmenting characters.(3) In the projection algorithm with template matching[8] [9], the number of white points in vertical direction is counted and recorded. The character region has more white points than the region of space between characters. By detecting the trough of white points between characters, the spaces can be located and then the boundary of characters can be located. Character Recognition uses the basic methods as pattern recognition and Neural Network based recognition systems.(1) In Pattern Recognition method[10], the character can be written differently so the pattern may varies. this may give false result.(2) Neural Network based method[11][12], this system needs training to recognise the characters. In this computational time depends on the training set and also it is very expensive.

III. STRUCTURE OF TYPICAL LPR SYSTEM

The typical LPR system consists of four major parts: Image capture, license plate location, character segmentation and character recognition. As shown in Figure 1.

Figure 1. Vehicle license Plate character recognition system block diagram

IV.IMPLEMENTATION

A. Image Acquisition

The first step is the capturing of an image using the USB camera connected to the PC. The images are captured in RGB format so it can be further process for the number plate extraction. Pre-processing of the captured image is performed such as RGB to gray scale conversion, noise filtering, Binarization process.

B. Location of License Plate

This plate localization algorithm is based on combining textural characteristics of license plate and morphological operation sensitive to specific shapes in the input image with a good threshold value by which the license plate is located. A fine percentage of localization of License plates is achieved by this algorithm. This is a better performing algorithm for License Plate Images with complicated background. License Plate consists of many vertical edges because it consists of Borders, Characters, and Digits. Sobel mask is used to detect vertical edges in the input image. Wavelet decomposition is performed to have better analysis. The resultant image is converted into a binary image. Morphological operations such as erosion and dilation are performed to find the location of the license plate.

C. Character Segmentation

In this paper, the segmentation of license plate characters is done after license plate location and binarization. A figure of license plate region after license plate location and binarization is displayed as Fig. 2. Suppose that the image data after license plate location and binarization is stored in matrix B. The number of columns of BW is stored in C. The number of rows of BW is stored in R.

Figure 2. License Plate after Location

1) Precise Location of the Top and Bottom Boundary

In this step, a scanline algorithm is used to accurately locate the top and bottom boundary of license plate characters. The scanline algorithm is based on the feature that the gray scale in character region changes frequently. Thus the total number of transition in character region is more than the total number of transition in other region. There are atleast seven characters in license plate region

Image Acquisition

Location of License Plate

Character Segmentation

Character Recognition


ISSN: 2230-7818 @ 2011 http://www.ijaest.iserp.org. All rights Reserved. Page 11

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Input B

and every character has more than two Jumps. So we can choose fourteen as a threshold value. To be on the safe side, we can choose twelve as the threshold value. If the total number of transitions in a certain line is greater than twelve, this line may be in character region. Otherwise, it is not in character region. The specific algorithm for precise location of the top and bottom boundary is as follows.

(a) Location of Top boundary

Scan the figure of license plate from the half of it to the top of it line by line. If the total number of Jumps in a certain line is less than twelve, it can be marked as the top boundary of character region.

(b) Locate the bottom boundary

Scan the figure of license plate from the half of it to the bottom of it line by line. If the total number of Jumps in a certain line is less than twelve, it can be marked as the bottom boundary of character region.

Figure.3. Algorithm for location of bottom boundary.

Figure.4. License Plate after precise location

2) Vertical Projection and Thresholding

Project the figure in vertical direction after precise location of the top and bottom boundary. Because black points in character region is much more than the black points in the region between characters, the result of projection appears continuous peak-trough-peak. The specific algorithm for vertical projection and thresholding is as follows.

(a) Vertical projection

Scan the figure from left to right column by column after precise location of the top and bottom boundary and count the total number of black points in every column. The result is stored in array projection . It is obvious that the length of Project is C.

(b) Thresholding

Suppose that one represents peak and zero represents trough. The threshold value is set to h/10. Judge every value in array projection . If Projection[i] is greater than h/10, Projection[i] is set to one. Otherwise, Projection[i] is set to zero. Where h is the modified no of rows of the binary image after precise location of top and bottom boundaries. Figure.5. Character segmentation

D. Character Recognition using template matching

In order to overcome the shortcomings of the simple template matching algorithm, low-resolution template matching method is adopted, namely the using of a lower pixel resolution (such as 4 x 8) to represent the images and templates to be recognized. Each matrix element corresponds to a sub-matrix in a high-resolution matrix. The element's value is the average of the pixel gray value in the corresponding high-resolution sub-matrix. Compared with the high-resolution matching algorithm, correct identification rate of the letters and numbers is greatly enhanced. The reason is that if the resolution rate goes through a moderate reduction, the error generated by the image distortion and the noise will be decreased. The recognition errors of letters and numbers mainly occur in

Bottom=Row

Row = R/2

Count Transitions

from 0 to1 and 1 to

0 and store it as n Row=Row+1

n>=12 Y

N



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some of the characters with the very similar main structures but some detailed differences, such as C and 0, 3 and 5. When we use the similarity method to calculate the matching degree, the evaluation of these values provided by these subtle differences is crucial. The use of high-resolution template matching method, these deviations in the relevant calculation have a serious interference of the evaluation of the value function, which makes the calculation unstable. When we use the low-resolution template matching method, the deviation of this alignment will have a small proportion in each element of a matrix. Therefore, in a considerable number of cases, it does not affect the structure description of the alpha-numeric and thereby increases the correct identification rate.

1) Calculation of Similarity

The similarity is function which is used to measure the degree of similarity between the patterns to be identified and templates. In the template matching method, the formula of similarity has important implications on the recognition results. In the low-resolution template matching method, using the similarity discriminate method mainly include two kinds: absolute distance and similarity. The absolute value distance regards the sum of the distance between image and the corresponding image pixel value of template as a function to measure the similarity. Given that image F and the template M are respectively image of X x Y, the formula to calculate the absolute value of the distance is as follows:

1 1

0 0( , ) ( , ) ( , )

X Y

i j

D F M F i j M i j

(1)

V. EXPERIMENTAL RESULTS

Finally the displayed license number is KA 19 P 8488.

VI. CONCLUSION

The ultimate goal is to obtain vehicle information from License Plate Recognition System and the recognition and extraction of the license plate is the key of parts in the vehicle monitoring processing. In this paper, it proposes and designs the algorithm of the image processing and segmentation, based on the analysis of the variety of character recognition system. Through experiments and improvements, the effect of the improvement is obvious and the character recognition rate increased substantially.

ACKNOWLEDGEMENT

This research was supported by grant from Department of TIFAC CORE in Telematics in V R Siddhartha Engineering College, Vijayawada..



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REFERENCES

[1] Optimization of vehicle licence plate segmentation and

symbol recognition, R.P. van Heerden and E C. Botha,

Department of Electrical, Electronic and Computer engineering, University of Pretoria, South Africa

[2] Robust License-Plate Extraction Method under ComplexImage

Conditions, Sunghoon Kim, Daechul Kim, Younbok Ryu, and Gyeonghwan Kim, Dept. of Electronic Engineering, Sogang University, Seoul, Korea

[3] Automatic Car Plate Recognition Using a Partial SegmentationAlgorithm, Fernando Martin, David Borges, Signal Theory and Communications Department, Vigo University, Pontevedra, Spain

[4] License plate recognition system, David Chanson and Timothy Roberts, Department of Electrical and Electronic Engineering, Manukau Institute of Technology, Auckland

[5] License Plate Recognition - Final Report, Pierre Ponce, Stanley S. Wang, David L. Wang.

[6] Hongwei Ying, Jiatao Song, Xiaobo Ren” Character Segmentation for License Plate by the Separator Symbol's Frame of Reference” 2010 International Conference on Information, Networking and Automation (ICINA)

[7] Deng Hongyao, Song Xiuli “License Plate Characters Segmentation Using Projection and Template Matching”, 2009 International Conference on Information Technology and Computer Science

[8] Shuang Qiaol , Yan Zhul , Xiufen Li l , Taihui Liu2 ,3, Baoxue Zhangl “Research of improving the accuracy of license plate character segmentation” 2010 Fifth International Conference on Frontier of Computer Science and Technology.

[9] Dong- June Lee, Seong-Whan Lee” A New Methodology for Gray-Scale Character Segmentation and Recognition”, 1995

IEEE. [10] Yucheng Li, Yisong Liu Mushu Wang “Study and Realization

for License Plate Recognition System”,2009 IEEE. [11] c. Nelson Kennedy Babu, Krishnan Nallaperumal “A License

Plate Localization using Morphology and Recognition”, 2008 IEEE.

[12] Baoming shan “License Plate Character Segmentation and Recognition Based on RBF Neural Network”,2010 IEEE



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