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    • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 2, March – April (2013), © IAEME 295 LICENSE PLATE RECOGNITION SYSTEM FOR INDIAN VEHICLES M. M. Kodabagi1 , Mr. Vijayamahantesh S. Kanavi2 1 Department of Computer Science and Engineering, Basaveshwar Engineering College, Bagalkot-587102, Karnataka, India 2 Department of Computer Science & Engineering, K.L.E. Institute of Technology, Hubli-580030, Karnataka, India ABSTRACT License Plate Recognition (LPR) has been intensively studied in many countries. Due to the different types of number plates being used, the requirements of an automatic number plate recognition system are different for each country. In general, objective of any LPR system is to localize and recognize potential license plate region(s) of the vehicle images captured from handheld device/digital camera/mobile phone camera. The proposed method for License Plate Recognition (LPR) system works in three modules: localization of license plate, segmentation of the characters and recognition of the characters from the license plate. Localization of the license plate is done using morphological operations, horizontal & vertical edge processing. Character segmentation is carried out using connected component labeling. Character recognition is done by using Neural Network classifier. The method is tested on 100 samples of Indian vehicle images. The system achieves 86% accuracy in localization, 81% accuracy in segmentation and 80% accuracy in character recognition. Keywords: Localization, Segmentation, Recognition, Neural Network 1. INTRODUCTION During the past few years, Intelligent Transportation Systems (ITSs) have had a wide impact in people’s life as their scope is to improve transportation safety and mobility and to enhance productivity through the use of advanced technologies. In the current information technology era, the use of automations and intelligent systems is becoming more and more widespread. The Intelligent Transport System (ITS) technology has received so much attention that many systems are being developed and applied all over the world. Therefore License Plate Recognition (LPR) has turned out to be an important research issue. INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET) ISSN 0976 - 6480 (Print) ISSN 0976 - 6499 (Online) Volume 4, Issue 2 March – April 2013, pp. 295-304 © IAEME: www.iaeme.com/ijaret.asp Journal Impact Factor (2013): 5.8376 (Calculated by GISI) www.jifactor.com IJARET © I A E M E
    • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 2, March – April (2013), © IAEME 296 License Plate Recognition has many applications in traffic monitoring system, including controlling the traffic volume, ticketing vehicle without the human control, vehicle tracking, policing, security, and so on. The most vital and the most difficult part of any LPR system is the detection and extraction of the vehicle number plate, which directly affects the systems overall accuracy. The presence of noise, blurring in the image, uneven illumination, dim light, variation in license plate sizes and foggy conditions make the task even more difficult [1]. Therefore the overall problem may be subdivided into three distinct key modules: (a) localization of license plate from vehicle image (b) segmentation of the characters within the license plate and (c) recognition of segmented characters within the license plate [4]. The main function of the module (a) is to find out the potential regions within the image that may contain the license plate. The function of module (b) is to isolate the foreground characters from the background within the detected license plate region. And the function of the module (c) is to recognize the segments in terms of known characters or digits. Though modules (b) and (c) employ most of the traditional methods available to the technologists, module (a) i.e. localization of potential license plate regions(s) from vehicle images is the most challenging task due to the huge variations in size, shape, color, texture and spatial orientations of license plate regions in such images. Hence, in this work a novel method is proposed for detecting the location of vehicle number plates and recognizing the numerals and characters in it. In this paper, a new model for License Plate Recognition (LPR) system is presented. The proposed method for License Plate Recognition (LPR) system works in three modules: localization of license plate, segmentation of the characters and recognition of the characters from the license plate. Localization of the license plate is done using morphological operations, horizontal & vertical edge processing. Character segmentation is carried out using connected component labeling. Character recognition is done by using Neural Network classifier. The method is tested on 100 samples of Indian vehicle images. The system achieves 86% accuracy in localization, 81% accuracy in segmentation and 80% accuracy in character recognition. The rest of the paper is organized as follows: The related work is discussed in section II. The proposed model is presented in section III. Experimental results are given in section IV. Section V concludes the work and lists future directions. 2. RELATED WORKS A substantial amount of work has gone into the research related to License Plate Recognition system. Some of the related works are summarized below. An approach for Automatic Number Plate extraction, character segmentation and recognition for Indian vehicles is proposed in [1]. In India, number plate models are not followed strictly. Characters on plate are in different Indian languages, as well as in English. Due to variations in the representation of number plates, vehicle number plate extraction, character segmentation and recognition are crucial. We present the number plate extraction, character segmentation and recognition work, with English characters. Number plate extraction is done using Sobel filter, morphological operations and connected component analysis. Character segmentation is done by using connected component and vertical projection analysis. Character recognition is carried out using Support Vector Machine (SVM). The segmentation accuracy is 80% and recognition rate is 79.84 %.
    • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 2, March – April (2013), © IAEME 297 A new technique for Automatic Number Plate Recognition is discussed in [2]. This new approach use the vehicle number plate image captured from the digital camera and the image is processed to get the number plate information. In this context, the number plate area is localized using a novel Feature-based number plate localization method mainly focusing on the two algorithms i.e., Edge Finding Method and Window Filtering Method. These methods are used for the better development of the number plate detection system. A new method for pixel based segmentation algorithm of the alphanumeric characters in the license plate is presented in [3]. This system can be used in many different countries –a feature which can be especially useful for trans-border traffic e.g. use in country borders etc. Additionally, there is an option available to the end-user for retraining the Artificial Neural Network (ANN) by building a new sample font database. The system was tested on 150 different number plates from various countries and an accuracy of 91.59% has been reached. Another method for License Plate Recognition system is presented in [4]. This new method used an innovative idea statistical distribution of the vertical edges for localization of license plate, connected component labeling for segmentation of the characters and template matching technique for recognition of the characters. In this method a real time vehicle images are captured from a road-side surveillance camera automatically throughout day and night. The images are stored in a centralized data server. A continuous process takes the stored images sequentially and interprets the license number of the vehicle. The performance of the system is measured at the three levels, i.e., localization level, segmentation level and recognition level and the result seems are quite satisfactory. A robust method for license plate location, segmentation and recognition of the characters proposed in [5]. The images of various vehicles have been acquired manually and converted in to gray-scale images. Then wiener2 filter is used to remove noise present in the plates. The segmentation of gray scale image generated by finding edges using Sobel filter and then bwlabel is used to count the number of connected component. Finally, single character is detected. The results show that the proposed method achieved accuracy of 98% by optimizing various parameters with higher recognition rate than the traditional methods. A new technique for real time application which recognizes license plates from cars at a gate is employed in [6]. The system is based on regular PC with video camera, catches video frames which include a visible car license plate and processes them. Once a license plate is detected, its digits are recognized, displayed on the user interface or checked against a database. The focus is on the design of algorithms used for extracting the license plate from a single image, isolating the characters of the plate and identifying the individual characters. The proposed system has been implemented using Vision Assistant 8.2.1 & Labview 11.0 the recognition of about 98% vehicles shows that the system is quite efficient. A new approach for the Automatic Recognition of Vehicle License Plates is presented in [7]. In this approach Multilayer feed-forward back-propagation algorithm using three hidden layers are used. The performance of the proposed algorithm has been tested on real vehicle images. Based on the experimental results, they conclude that their algorithm shows superior performance using two hidden layers than single hidden layer and the results revealed that as the number of hidden layers is increased, more accuracy is achieved. A new method for License Plate Recognition System proposed in [8]. This method uses vehicle steps over magnetic loop detector that senses car and takes image of the car, following image preprocessing operations for improvement in the quality of car image. From this enhanced image, license plate region is recognized and extracted. Then character fragmentation/segmentation is performed on extracted License Plate and those segmented characters are recognized using Neural Network.
    • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 2, March – April (2013), © IAEME 298 An algorithm based on a combination of morphological operation with area criteria tests for number plate localization is presented in [9]. In this method segmentation of the number plate characters is 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. The system was experimented with four different edge detectors namely Sobel, Canny, Prewitt, LOG. A comparative analysis on the success rate of the proposed system showed overall better success rate of 96.8% by using canny edge detector. An approach for Automatic Number Plate Recognition system discussed in [10]. This approach consists of few algorithm like “Feature based number plate Localization” for locating the number plate, “Image Scissoring” algorithm for character segmentation and proposed algorithm for character recognition using Support Vector Machine (SVM).System can recognize single or double line number plate. The problem of license plate recognition using a three layer fuzzy neural network is presented in [11]. In the first stage the plate is detected inside the digital image using rectangular perimeter detection and the finding of a pattern by pattern matching, after that, the characters are extracted from the plate by means of horizontal and vertical projections. Finally, a fuzzy neural network is used to recognize the license plate. The tests were made in an uncontrolled environment in a parking lot and using Mexican and American plates. The results show that the system is robust as compare to those systems Papered in the literature. A new method for Vehicle License Plate Identification is employed in [12]. In this method an adaptive image segmentation technique (sliding concentric windows) and connected component analysis in conjunction with a character recognition neural network. The algorithm was tested with 1334 natural-scene gray-level vehicle images of different backgrounds and ambient illumination. The camera focused in the plate, while the angle of view and the distance from the vehicle varied ac-cording to the experimental setup. The license plates properly segmented were 1287 over 1334 input images (96.5%). The optical character recognition system is a two-layer probabilistic neural network (PNN) with topology 108-180-36, whose performance for entire plate recognition reached 89.1%. The PNN is trained to identify alphanumeric characters from car license plates based on data obtained from algorithmic image processing. Combining the above two rates, the overall rate of success for the license-plate-recognition algorithm is 86.0%. Out of many works cited in the literature it is agreed that existing algorithms based on localization, segmentation and recognition of characters in vehicle license plates are having some limitations due to, variations in the representation of number plates, color of the plate, font/size of each character, spacing between subsequent characters etc. To address these challenges the objective of current work is to develop a novel technique for localization, segmentation and recognition of license plate not only to enrich the research but also used in full-fledged commercial environment. 3. PROPOSED MODEL The proposed model comprises in two main phases, Training and Testing. The Training phase covers preprocessing of training images, zone-wise statistical feature extraction, knowledge base construction. The Testing phase covers preprocessing of car image, number plate localization, character segmentation and recognition model. The block diagram of the proposed model is given in Fig. 1. The detailed description of each phase is presented in the following subsections.
    • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 2, March – April (2013), © IAEME 299 . Fig. 1. Block Diagram of Proposed Model The images are collected from various vehicles captured from mobile phone camera (2 MegaPixels). The characters from those license plates are segmented manually and stored as training images/samples in the database for training neural network. 3.1 Preprocessing of training image The character image from the database is preprocessed for enhancing the recognition accuracy of the proposed work. The steps of preprocessing are as follows: • Convert the input image into black and white image using the function im2bw. • Salt and pepper noise are removed by calling the medfilt2 function. • Image is resize to 30*30 by using the function imresize.. • Generate thinned image by calling the function bwmorph. 3.2 Feature Extraction The preprocessed image is collected from database then it is divided into various blocks/zones with the size 5 X 5 as shown in Fig. 2.2. The sum of all pixels in the zone is computed which represent one feature/value of an image. Totally 36(30 * 30 size of the image) features are computed in the same manner for an entire image and features are stored in the feature vector X. The feature vector X is described in equation (1). X = [ fi ]; 361 ≤≤ i (1) Where, fi is feature vector of ith zone.
    • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 2, March – April (2013), © IAEME 300 3.3 Knowledge Base Construction. From the database, 50% of samples are used for training and 50% of samples are used for testing. Then the feature vectors of all training images are stored into knowledge base KB as shown in equation (2). KB = [ Xj ]; Nj ≤≤1 (2) Where • KB is knowledge base, • Xj is a feature vector of jth image in the training dataset • N is the number of training samples. 3.4 Training the Neural Network The Knowledge base is given as input to the Neural Network for training of samples. 3.5 Preprocessing of vehicle image In this process Colored Image is converted into Gray-Scale Image. The algorithm described here is independent of the type of colors in image and relies mainly on the gray level of an image for processing and extracting the required information. Color components like Red, Green and Blue value are not used throughout this algorithm. So, if the input image is a colored image represented by 3-dimensional array, it is converted to a 2-dimensional gray image before further processing. 3.6. Number Plate Localization Dilation is a process of improvising given image by filling holes in an image, sharpen the edges of objects in an image, and join the broken lines and increase the brightness of an image. In this process, each pixel is compared with its neighboring pixels and its value is set equal to the maximum value out of both the neighboring pixels. This process makes edges of an image sharper. In turn, it helps in better detection of an image. If an input image is blurred, this step will help to improve such blurred image and make it easy for detection. In horizontal edge processing of an image, the algorithm traverses through each column of an image. In each column, the algorithm starts with the second pixel from the top. The difference between second and first pixel is calculated. If the difference exceeds certain threshold, it is added to total sum of differences. Then, algorithm will move downwards to calculate the difference between the third and second pixels. So on, it moves until the end of a column and calculate the total sum of differences between neighboring pixels. At the end, an array containing the column-wise sum is created. The same process is carried out to find the vertical edge processing. In this case, rows are processed instead of columns. To prevent loss of important information in further steps, it is required to smooth out such changes in values of histogram. For the same, the histogram value is passed through a low-pass digital filter. While performing this step, each histogram value is averaged, considering the values on it right-hand side and left-hand side. This step is performed on both horizontal as well as the vertical array values. This step finds all the regions in an image that has high probability of containing a license plate. Co-ordinates of all such probable regions are stored in an array.
    • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 2, March – April (2013), © IAEME 301 The output of segmentation process is all the regions that have maximum probability of containing a license plate. Out of these regions, the one with the maximum histogram value is considered as the most probable candidate for number plate. All the regions are processed row-wise and column-wise to find a common region having maximum horizontal and vertical histogram value. This is the region having highest probability of containing a license plate. 3.7 Character segmentation In character segmentation module, global threshold (level) is computed that can be used to convert an intensity image to a binary image. All pixels in the input image with luminance greater than a level value with the value 1 (white) and replace all other pixels with the value 0 (black). Label connected components Once we got the a threshold value the next step is to remove from a binary image all connected components (objects) that have fewer than 30 pixels, producing another binary image. The bwareaopen function used in this work for such purpose. The default connectivity is 8 for two dimensions. Measure properties of image regions The next step is used to measure properties of image regions. The image is a logical array; it can have any dimension. In grayscale image Bounding Box method is used, which returns the smallest rectangle containing the region. The method takes two parameters, one to specify the upper-left corner of the bounding box and another specifies the width of the bounding box along each dimension. Once the properties are measured a rectangle box is drawn on each of the object identified. Objects extraction In the object extraction, the find function returns the row and column indices of the nonzero entries in the matrix X. This syntax is especially useful when working with sparse matrices. Individual objects are extracted one by one and these characters are held in an array for further processing. 3.8 Character Recognition Model In this phase, a testing image is processed to obtain zone-wise features and stored into a feature vector T as shown in equation (3). T = [ fi ]; 361 ≤≤ i (3) Character classifier in this system is based on K-Nearest Neighbors (KNN). The design of KNN based character classifier is shown in figure 2.
    • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 2, March – April (2013), © IAEME 302 Figure 2 KNN-based Character Classifier The KNN classifier uses features of testing image returns recognized character. 4. EXPERIMENTAL RESULTS AND ANALYSIS The experimental tests were conducted for most of the images containing several issues like spatial orientation, double lined characters, tilted license plates and results were highly encouraging. The experimental result of processing one of the issue i.e license plate with spatial orientation is considered as an example and the overall performance of the system are given below; Fig 3.Image with license plate at Fig 4. After Gray Scale conversion spatial orientation Fig 5 After localization phase Fig 6. After Segmentation phase Fig 7. Recognised Characters
    • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 2, March – April (2013), © IAEME 303 TABLE 1: Overall System Performance 5. CONCLUSION The work presented in this paper involved developing a License Plate Recognition system in order to verify character recognition of the vehicle number plate. From the experimental results it is observed that this method gives the good results. The method is tested for 100 samples of vehicle image database. This technique is not only computationally less expensive but also provides best recognition results. The method is insensitive to the presence of skew, different font size/style, double lined number plate and achieves 86% accuracy in localization,81% accuracy in segmentation and 80% accuracy in character recognition accuracy. Several open questions still remain in this license plate recognition system. The robustness for image variations in rotations, illumination, etc. must be improved. REFERENCES [1] M. M. Shidore, S. P. Narote, ”Number Plate Recognition for Indian Vehicles”, IJCSNS International Journal of Computer Science and Network Security, VOL.11 No.2, Feb. 2011, pp.143-146. [2] S.Kranthi, K.Pranathi, A.Srisaila, “Automatic Number plate Recognition”, International Journal of Advancements in Technology, Vol 2, No. 3, July 2011, pp. 408-422. [3] Ankush Roy, Debarshi Patanjali Ghoshal, “Number Plate Recognition for use in Different Countries Using an Improved Segmentation”, 978-1-4244-9581-8/11/$26.00 © 2011 IEEE. [4] Satadal Saha, Subhadip Basu and Mita Nasipuri, “Automatic Localization and Recognition of License Plate Characters for Indian Vehicles”, IJCSET, Vol-2 No 4 August 2011, pp. 520-533. [5] Anish Lazrus, Siddhartha Choubey, Sinha G.R.”An Efficient Method of Vehicle Number Plate Detection And Recognition”, IJMI International Journal of Machine Intelligence ISSN: 0975–2927 & E-ISSN: 0975–9166, Volume 3, Issue 3, 2011, pp-134-137. [6] Kuldeepak, Monika kaushik, Munish Vashishath, “License Plate Recognition System based on Image Processing Using Labview”, International Journal of Electronics Communication and Computer Technology (IJECCT), Volume 2 Issue 4 (July 2012), pp. 183-188. [7] Stuti Asthana, Niresh Sharma, Rajdeep Singh, “Vehicle number plate recognition using multiple layer back propagation neural networks”, International Journal of Computer Technology and Electronics Engineering (IJCTEE) Volume 1, Issue 1, pp.35-38. Total Number of Images used for Testing Localization Model Segmentation Model Character Recognition Model 100 86% 81% 80%
    • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 2, March – April (2013), © IAEME 304 [8] Anuja P. Nagare, “License Plate Character Recognition System using Neural Network”, International Journal of Computer Applications (0975 – 8887), Volume 25– No.10, July 2011 pp.36-39. [9] P.Anishiya, Prof. S. Mary Joans, “Number Plate Recognition for Indian Cars using Morphological Dilation and Erosion with the Aid of Ocrs” , 2011 International Conference on Information and Network Technology IACSIT Press, Singapore, IPCSIT vol.4 2011, pp.116-119. [10] Shriram Kishanrao Waghmare, A. K. Gulve, Vikas N.Nirgude, “Automatic Number Plate Recognition (Anpr) System For Indian Conditions using Support Vector Machine (Svm)”, International Journal of Computer Science and its Applications [ISSN 2250 - 3765], pp.216-220. [11] Osslan Osiris Vergara Villegas, Daniel Gonzál ez Balderrama, Humberto de Jesús Ochoa Domínguez and Vianey Guadalupe Cruz Sánchez, ”License Plate Recognition Using a Novel Fuzzy Multilayer Neural Network”, International Journal of Computers, Issue 1, Volume 3, 2009, pp.31-40. [12] Christos Nikolaos E. Anagnostopoulos, Ioannis E. Anagnostopoulos, Vassili Loumos, and Eleftherios Kayafas, “A License Plate-Recognition Algorithm for Intelligent Transportation System Applications”, IEEE, Transactions on Intelligent Transportation Systems. Vol. 7, No.3, September 2006. pp. 377-392. [13] Mayur V. Parulekar, Viraj Padte, Dhaval Shroff, Akash Metawala and Harsh Nangalia, “Congestion Control & Collision Avoidance Algorithm in Intelligent Transportation Systems”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 2, 2013, pp. 114 - 124, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [14] M. M. Kodabagi, S. A. Angadi and Chetana. R. Shivanagi, “Character Recognition of Kannada Text in Scene Images using Neural Network”, International Journal of Graphics and Multimedia (IJGM), Volume 4, Issue 1, 2013, pp. 9 - 19, ISSN Print: 0976 – 6448, ISSN Online: 0976 –6456. [15] M. M. Kodabagi and S. R. Karjol, “Script Identification from Printed Document Images using Statistical Features”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 2, 2013, pp. 607 - 622, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [16] M. M. Kodabagi and Hemavati C. Purad, “Script Identification using DCT Coefficients”, International Journal of Graphics and Multimedia (IJGM), Volume 4, Issue 1, 2013, pp. 31 - 40, ISSN Print: 0976 – 6448, ISSN Online: 0976 –6456. [17] M. M. Kodabagi, S. A. Angadi and Anuradha. R. Pujari, “Text Region Extraction from Low Resolution Display Board Images using Wavelet Features”, International Journal of Information Technology and Management Information Systems (IJITMIS), Volume 4, Issue 1, 2013, pp. 38 - 49, ISSN Print: 0976 – 6405, ISSN Online: 0976 – 6413.