Automatic Vehicle Number Plate Recognition Using ...

2015 IEEE Conference on Systems, Process and Control (ICSPC 2015), 18 - 20 December 2015, Bandar Sunway, Malaysia

Automatic Vehicle Number Plate Recognition Using Structured Elements Riazul Islam, Kazi Fatima Sharif and Satyen Biswas Dept. of Electrical and Electronic Engineering, Ahsanullah University of Science & Technology, Dhaka, Bangladesh {[email protected], [email protected], [email protected]} Abstract—Automatic Number Plate Recognition (ANPR) is a kind of image processing technology for recognizing the vehicle number plate. This system also offers users to place, mark out and monitor moving vehicles automatically by extracting their number plates. It also plays an important role in intelligent traffic control system. This research presents a prosperous method to identify vehicle number plates. The proposed technique is built on morphological operations based on different structuring elements in order to maximally exclude noninterested region and improve object area. This system has been experienced using a database of number plates and simulated results demonstrate major improvements as compared to other conventional systems. The success rate of the proposed method is about 92% with varying light conditions.

al. [6]. They used optical character recognition (OCR) system but it consumed enough time in recognition of characters. Nijhiset.al [7] proposed a new method based on fuzzy logic using neural networks to identify car license plate. But their system needs to train large elementary data. Lee et. al. [8] also introduced a time swallowing technique using optical character recognition. They proposed genetic algorithm based segmentation but it did not perform well due to more time for implementation. Bidirectional associative memory neural network (BAM) based system was proposed by Fahmy et. al. [10].Color extraction and characters recognition based template comparison system was presented Lee et. al.[11]. Kim et. al. [12] proposed a technique based on Hough transform with only vertical edges or border to extract vehicle license plate. All those system require a huge number of preprocessed data that has poor segmentation. The proposed system uses an optical character recognition (OCR) device, which is used to read character from the image of license plate. It consists of a camera or frame grabber that captures an image of a vehicle and finds the location of the number plate. And then identify the characters according to character recognition tool to convert into numerically identified character. The proposed system is designed to be light entrusting so that it can be use in real time under general condition. The proposed algorithm of automatic number plate recognition is divided into following steps: (a) taking picture from the vehicle, (b) processing the image, (c) character extraction from the number plate, (d) character segmentation, (e) character matching, (f) identify the number plate, (g) output. The typical flow diagram of the system is shown in Figure 1. In the proposed system, a camera or frame grabber is installed for collecting an image frame. An automatic method is followed for analysis and recognizes the number plate within an estimated time under existing illumination condition. By comparing with the database, a decision and output is formalized accordingly. It performs the two major task: (a) License plate number location using canny detector and (b) Recognition of number plate with template matching to identify each and every character (A to Z), numerical value (0 to 9) and making a separate sign (‘-‘). When all these tasks are completed successfully then it is matched with the database information stored. Section 2 explains the design procedure; section 3 demonstrates the results and section 4 concludes the technique.

Keywords—Automatic Number Plate Recognition; Character Segmentation; Character Recognition. Template Matching

I.

INRODUCTION

Vehicle Number Plate recognition carries an significant role in different applications for example traffic monitoring on highway, automatic toll fee, parking lots access control, identification of plundered vehicles etc. It was first employed in 1976 in United Kingdom at a police station. Prototype systems were introduced in 1979 and contracts were bestowed conducting commercial systems. This type of modern secured technology is now used in various restricted areas, such as parliament house, military area, Supreme Court and so on. Automatic number plate recognition from the standard number plate is very easy to recognize. But it is very tough to identify if it has no standard size and pattern. Therefore it requires a competent algorithm for better. Several techniques were proposed to improve the system by many research groups [1-12]. To identify a vehicle number plate, a competitive and suitable method was proposed by a group [1]. Very similar techniques were also proposed by other two research groups [3-5]. Their system executed only for specific country or state and showed very poor performance in case of other country or unable to recognize number plate. Even if the system was able to identify the location of number plate it did not recognize the character because of the variable characteristic of number plate existed on different formation. Also most of the techniques were time consuming due to computing complexity. They employed edge detection algorithm, Neural network analysis and/or Hough transform algorithm in their system and that caused high computing time. Recently a new technique was proposed by Johnson et.

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Judgement /Decision

(e) Edge approaching by hysteresis: The end point of edges are determined by deleting all edges (an original image is shown in Figure 6) those are not connected to a very true (strong) edge, as shown in Figure 7.

Database

In the image, all the actual edges are calculated even some of the edges in the surrounding, like edges or margin of tree or railing are also marked and getting an edge image as shown in Figure 7.

Image Processing Detail/Message

Candidate Vehicle Plate Area Identification: Morphological Operation is applied for removing the irrelevant objects in the image. Dilation and erosion are performed in order to extract desired plate areas from the processed image. Some unrelated objects areas are still considered as desired postulant plate. For removing those unwanted candidates in the image, an algorithm presented by Lee et. al.[11] is employed.

Camera

Vehicle

Vehicle Plate Character Segmentation: It is must to recognize the vehicle number accurately, which is mostly dependent on the character separation or isolation. So all the character from the image are separated without losing any element of a character. The segmentation step in ANPR system consider the analysis of character pattern, region and adjoin element.

Fig. 1: Basic Block Diagram of ANPR

Vehicle Plate Character Area Enhancement: For enhancing the number plate character area, proper segmentation is needed. Appropriate segmentation is done by selecting correct threshold values. Thresholding effect is shown in Figure 8.

II. PROPOSED METHOD OF ANPR First of all, Image frame is captured by a high resolution camera from the distance of 4 to 5 meters from the vehicle. Then the presented system locates the object area by employing canny edge detector and morphological operation [Ref]. Finally each and every alpha-numeric characters presented in the object area are identified using template matching with the help of prearranged database of all individual characters. The flowchart of the proposed Algorithm is given in Fig. 2.There are several important steps to be followed in order to recognize the characters of a number plate as shown in Figure 3.

Start

Input image Preprocess Localization

Image Acquirement and Preprocessing: The ANPR system requires a high resolution camera to acquire an image. Images are taken at various illumination, background and environment conditions. Also it is captured at different distance from the vehicle. Then the image frames are resized to a predefined (400 X 600) size and all the processing steps are executed on gray scale image as shown in Figure 4. The purpose of this is to enhance the processing speed, increase the contrast of image and existing noise. The image frames are filtered to eliminate the noise after resizing and converting to gray scale image.

No Is vehicle plate find out? No

Yes OCR

Identifying the Edge: The image frames are then processed by using some competent algorithms in order to recognize the object. The plate area edge is identified by using ‘Canny detector’. Then the image is converted into binary image as shown in Figure 5. For sorting out all the rear noise and conserve the number plate area in the image frames are passed through the median filter. Canny detection algorithm is performed through following five steps: (a) Smoothing: Eliminate the noise by staining. (b) Locating gradients: The large gradients of the image are calculated and marked to identify edges. (c) Non-maximum containment: Only local maxima are calculated to be the edges. (d) Double threshold: Powerful edges are ascertained by fixing the threshold at 0.5.

Is vehicle number acknowledged? Yes Comparing with stored database Result dispayed 45 Fig. 2: Algorithm Design of ANPR


Start

Load image Preprocessing of the image Fig. 5: Filtered Image

Extracting vehicle number plate Recognition of characters Number identified Store the required database

Fig. 6: Captured Image

Compared with database stored End

Fig. 3: Proposed Design Pattern. Fig. 7: Detecting Edges Using Canny Detector

Fig. 8: Noise Enhancing Character Fig. 4: Gray Scale Image

Attached Componenet Study: Connected component analysis algorithm is used to remove the noise from the object (characters on the plate). Each labeled matrix of 8-connectivity pixels is evaluated based on the area. The result of connected component analysis and noise removal is as shown in Fig. 4. Attached element or component study is used to eliminate the noise. 8-connected pixels is appraise based on the places of every labeled matrix. One of the resulting images is shown in Figure 9. Fig. 9 : Connected or Attached Element Study

Vertical Position Study: Character segment is used to separate the character from number plate. Vertical blank area between two consecutive alpha-numeric characters in the image is considered the separation line. After separation, each row and column data are stored. This module separates each and every 46


An original image frame is shown in Figure 12(a) which is captured by a camera. After removing the noise from the image, the identified objects or characters are shown in Figure 12(b). The final results of structural matching with the sample database are shown in Figure 12(c).

alphabet and number on the vehicle number plate designed on horizontally decorated in one row.

Fig. 10: Vertical Position With Respective Segmented Characters

Vehicle Plate Number Recognition: In ANPR system it is the most significant and critical stage to generate proper output. The previous steps are responsible for detecting the pattern of characters from captured image. The segmented number plate characters are rescaled to resemble the characters within a window. Each vehicle number plate character is converted to binary image with proper size and standard dimension before additional processing steps are applied. In this technique characters are identified by comparing the similarity of object or character element. In order to compare the alphanumeric characters with the database, correlation coefficients are computed and analyzed as follows:

xr[i ][ j ] =

p< mr / 2 q< mc / 2

∑

∑

p =− mr / 2 q = − mc / 2

( Msk [q + mc / 2][ p + mr / 2] − Msk ) (Im[i + q][ j + p] − Im)

where, Im is the image of the alphanumeric character collected from the vehicle plate and Msk is the image of the data base character. And the mean value of the pixels _____ brightness of the image and mask are represented by Im and _____

respectively. Using the vertical blank line, each and every character is separated. These individual characters are then identified by comparing with the pre stored database in the system. Different structures of each object elements are used for better performance. .Because there is lots of dissimilarities in the character shape in different parts of the world. Msk

Fig. 11: Flow Chart for Template Comparison

The output experimental results are shown in Table. 1. The result demonstrates a very promising success rate of about 92%. And the maximum computation time was about 0.3 sec. The causes of failure in some cases were mainly due to the abnormal size of the number plate or the images were captured from a faraway distance. The performance of a conventional algorithm was 89.2% success rate, while the proposed algorithm provides higher recognition rate. The presented system is also computationally inexpensive as compared to other conventional methods.

Table 1: Performance of System

Database Size

150

Successful Recognition

Recognition Rate

138

92%

III. EXPERIMENTAL RESULTS The proposed algorithm of the system was implemented by MATLAB. The validity, suitability and the effectiveness of the proposed system were examined through real experiments. A strong database of about 150 different images was used for simulation. Those raw images were collected from different areas, such as: highway, local road, side of the road, parking areas etc. Images were also acquired from different distances and partial orientation. However, all the images were of initial size 640*480 pixels. 47


method is so competent that it doesn’t matter whether the vehicle is in stationary or running at a high speed. It is also able to identify the characters in the plate even if the number plate is oriented up to 45 degrees. The presented technique can be used in cosmopolitan area, toll booth and any protected parking lot etc. The system also can be used in different countries just by modifying the database

References [1] Qadri, Muhammad Tahir, and Muhammad Asif. "Automatic number plate recognition system for vehicle identification using optical character recognition." In Education Technology and Computer, 2009.ICETC'09. International Conference on, pp. 335338. IEEE, 2009. [2] Kim, Kl Kim, K. I. Kim, J. B. Kim, and H. J. Kim. "Learningbased approach for license plate recognition."In Neural Networks for Signal Processing X, 2000.Proceedings of the 2000 IEEE Signal Processing Society Workshop, vol. 2, pp. 614-623.IEEE, 2000. [3] V. Kasmat, and S. Ganesan, “An efficient implementation of the Hough transform for detecting vehicle license plates using DSP’s,” IEEE International Conference on Real-Time Technology and Application Symposium, Chicago, USA,pp.58-59, 2005. [4] S.H. Park, K.I. kim, K. Jung and H.J. Kim, “Locating car license plate using Neural Network,” Electronic Letters, Vol. 35, No. 17, pp. 1474 – 1477,1999. [5] Kaur, Kavneet, and Vijay Kumar Banga. "AUTOMATIC VEHICLE NUMBER PLATE SEGMENTATION AND RECOGNITION." [6] R.A. Lotufo, A.D. Morgan, and AS. Johnson, Automatic NumberPlate Recognition, IEE Colloquium on Image Analysis for Transport Applications, V01.035, pp.1-6, February 16, 1990. [7] J.A.G. Nijhuis, M.H. TerBrugge, K.A. Helmholt, J.P.W. Pluim, L. Spaanenburg, R.S.Venema, M.A. Westenberg, 1995, Car License Plate Recognition with Neural Networks and Fuzzy Logic, IEEE International Conference on Neural Networks, 1995 [8] H.J. Kim, D.W. Kim, S.K. Kim, J.V. Lee, J.K. Lee, Automatic Recognition of Car License Plates Using Color Image Processing, Engineering Design & Automation, 3(2), pp. 215- 225, 1997 [9] S.K. Kim, D.W. Kim, and H.J. Kim, A Recognition of Vehicle License Plate Using a Genetic Algorithm Based Segmentation, 3rd IEEE International Conference on Image Processing, V01.2., pp. 661-664, 1996. [10] M.M.M. Fahmy, Automatic Number-plate Recognition Neural Network Approach, Vehicle Navigation and Information System Conference, 30,Aug- 2 Sept, 1994 [11] E.R. Lee, P.K. Kim, and H.J. Kim, 1994, Automatic Recognition of a Car License Plate Using Color Image Processing, International Conference on Image Processing (ICIP’94), Vol. 2 pp.301-305, 1994. [12] H.S. Kim, et al. Recognition of a Car Number Plate by a Neural Network, Korea Information Science Society Fall Conference, Vol. 18, pp. 259-262, 1991

(a) Original image

(b) Desired objects before matching

( c) Desired objects are opened by the text file after matching Fig. 12: Captured Image, Localizationa and OCR

IV. CONCLUSION This paper presents an efficient and first computing technique for identifying vehicle number plate. In this technique, limited amount of computations are employed in the algorithm. So it is computationally very inexpensive as compared with most of the conventional methods. The proposed system is capable to recognize any type of number plate within a fraction of a second (Less than 0.5 sec). The

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