2nd EEE Undergraduate Project Workshop EUProw 2011, Apr. 30 – May 02, BUET, Dhaka, Bangladesh
Development of an Automatic Vehicle License Plate Detection and Recognition System for Traffic Management in Bangladesh Nahian Alam Siddique, Asif Iqbal, Fahim Mahmud, Md. Saifur Rahman* Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh E-mail:
[email protected] No object should obstruct the view of the license plate. The contrast between dark and bright portions of the image is high enough for proper detection. The plate is rectangular shaped with 4 distinct corners.
Abstract– This paper describes the technique of extracting vehicle license plate and interpretation of the registration code from a captured image in real-time. The developed algorithm is divided into three stages: extraction of vehicle license plate from captured image, segmentation of license plate components and recognition of the license plate characters for interpretation of vehicle registration code. A control algorithm is also developed to choose between the recognition processes and reconstruct the registration code from recognized characters. Most of the processes are implemented using mostly logical operations to achieve lower execution time.
III. EXTRACTION AND SEGMENTATION It is not a trivial problem because of different plate sizes, different fonts used and complexity of the scene. In general, this algorithm looks for rectangular shapes. Horizontal and vertical edge detection method has been used for this part. In order to alleviate the problem of low quality and low contrast in car images, first the images are passed through a preprocessing stage to enhance car plate region. Though the exact location of license plate is unknown at this stage, we locally enhance image at regions with similar characteristic of a license plate.
I. INTRODUCTION In recent years, Government of Bangladesh is trying to impose E-government in Bangladesh. Automatic Vehicle License Plate Detection and Recognition (AVLPDR) system plays a vital role for an intelligent traffic management system. In license plate recognition, usually three steps are required: the first is the license plate extraction, regardless of the license-plate size and orientation; the second step is the segmentation of the characters in the plate and the normalization of other factors like brightness, contrast, illumination, etc. and the final step is recognition of the license plate characters to obtain the license number. Alternative approaches to detect and recognize vehicle license plates were developed by Mashuk et. al.[1] and Deb et. al. [2] These steps are discussed in the following sections.
A. Pre-processing stage: At first the acquired image was converted [4] to a gray-scale image because no color information was required in the plate region detection. Next, the image was resized to particular size in which license plate is recognizable and calculation is faster. In the third step the contrast was enhanced for the plate regions. At first contrastlimited adaptive histogram equalization method was applied for histogram normalization and later it was enhanced to distinguish high and low contrast easily using following equations: G[(a100)]=0 … … (2)
II. STUDY OF LICENSE PLATES Before continuing with the study, some practical license plates were analyzed.
Here,
A. Characteristics of license plates in Bangladesh Each license plate contains two lines. Most license plates contain white text on black background or black text on white background. There is a parallelogram-shaped border around the text. The border is of opposite color of the background color. Most texts are written in Bangla as per regulation, but some licenses are found to have used English.
a = original gray scaled image and b = CLAHE processed image
B. Locating the probable region of license plate The extraction process involves a several straightforward process to determine the exact location of the license plate. Sobel edge operator [5] was used to detect edges of objects in the captured image. Later horizontal and vertical edges were obtained using the following corresponding equations. X i, j X i 1, j & X i 1, j & X i, j … (3) Y i, j Y i, j 1 & Y i, j 1 & Y i, j … (4)
B. Assumptions for the Implementation The developed algorithm assumes certain characteristics to be applicable for the captured image of the license plate and characters. The image may contain other objects, similar in shape to the license plate. Despite variations in illumination level, the image quality should be sufficient for correct recognition of license plate characters. In the image, the plate should be a rectangular region mostly filled with characters.
Then from the identified horizontal edge lines only few were selected, based on a specific length determined by an adaptive calculation on objects from the image. Consecutively, vertical lines normal to the corresponding horizontal lines were examined against location and size property. The areas enclosing the particular horizontal and vertical lines were taken into account for the final checking.
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C. Segmentation
C2
sign C2' n2
st n 2
ri 2 2ri 1 ri
… … (8) Here, n = no. of elements in segment of the perspective, ri = magnitude of row in the ith column and st=column index of start of corresponding segment–1
Each particular area found, was first converted to BW image using Otsu’s method [4] and later several noise filtering and normalization were applied. Then they were searched for 2 horizontal regions; where each region was again searched for the components according to license plate regulations of Bangladesh. The characters or numbers from this elected license plate region were then segmented and passed to the recognition procedure. The search and segmentation procedure were based on the 8-pixel connectivity, line space and size properties.
i st 1
Sign of C2 represents the tendency of curvature, concave or convex. Using this model, the sample image is compared against the reference short-listed candidates and the matching candidate is returned as the output. If no candidate or more than one candidate is matched then an error is generated.
V. EXPERIMENTAL RESULTS
IV. CHARACTER RECOGNITION
All the experiments were conducted in MATLAB development environment using images captured from real scenarios and under different weather and lighting conditions. Experimental test results show that execution time and accuracy rate in detecting the number plates are quite satisfactory. An accuracy of 85% and the execution times below 3.0 seconds were achieved.
License plates in Bangladesh are written in two lines: the first one is in letters or words and the second one is in numbers. Therefore, two algorithms were implemented in detecting Bangla letters and numbers. Bangla Characters were first grouped based on four properties: (Top horizontal line), vertical line, upper extension and number of loops in the image (Euler Number-existence of at least a loop or hole in the construction of the letter). After grouping the characters, each letter in the group is recognized using several distinctive and particular mathematical characteristic. Accordingly a database of the vertical line’s position of the character is maintained to match character images against that. Using this parameter and can be recognized. Differentiation between and are in the top right portion, just beneath , where has a concave curve segment, that’s missing in . The detection of numbers is processed in a different algorithm. The perspective achieved from viewing the number image from any one side (namely top, right, down or left) is a greatly simplified representation of the main image, which still contains some key features of the image [Figure 1]. The advantage of this simplified perspective is that, it can be mathematically modeled and thus can be very easily compared with library references without any confusion.
Detected: Dhaka Metro- Ga 27 32 50
Detected: Dhaka Metro- Ga 11 04 76
Figure 2: Sequential processing of two sample images
VI. CONCLUSION In this research work an algorithm was developed for detecting the vehicle license plates used in Bangladesh, which may be easily implemented. Despite irregularity in license plate formation, an acceptable accuracy and performance level could be achieved. Further research can be undertaken to develop a centralized vehicle monitoring system along with the necessary hardware setup.
Figure 1: Different perspectives of a single character.
In the next step, different break points, corresponding to view angles are segmented. The set of breakpoints itself constitute a model of the character image, e.g. for the case of
REFERENCES [1]
the top view, a ,
or
should not contain any breakpoint
while a must contain one breakpoint. Breakpoints are used to create a shortlist of probable characters. The comparison of the shortlist involves a sub-process, which is used for modeling the segments. The model calculates following parameters: dc offset (C0), coefficient of first derivative (C1) and coefficient of second derivative (C2). C0
1 st n ri n i st 1 …
… (5)
C1
1 st n1 ri1 ri n 1 st i 1 …
1 st n 2 C ri2 2ri1 ri n 2 i st 1 … ' 2
[2]
[3]
[4]
… (6)
[5]
… (7)
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M.S. Mashuk, M.A. Majid, N. Basher, T.R. Rahman, “Automatic Detection of Bangla Characters in Bangladeshi Car Registration Plates”, 2010 2nd ICCIMSim, pp. 166-171, 2010 K. Deb, H.U. Chae, K.H. Jo, “Vehicle License Plate Detection Method Based on Sliding Concentric Windows and Histogram”, Journal of Computers, 4(8), pp. 771, 2009 A.R. Forkan, S. Saha, M. Rahman, A. Sattar, “Recognition of Conjunctive Bangla Characters by Artificial Neural Network”, Information and Communication Technology, 2007. ICICT '07. International Conference on, pp.96, 2007 Kenneth R. Castleman. “Digital Image Processing”. Prentice Hall, 1996 W. Zhang and F. Bergholm (1997) “Multi-scale blur estimation and edge type classification for scene analysis”, International Journal of Computer Vision.
