03-09-2014, 09:48 AM
Automatic Number Plate Recognition
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Abstract
Automatic Number Plate Recognition (ANPR) is a mass surveillance system that captures the image of vehicles and recognizes their license number. ANPR can be assisted in the detection of stolen vehicles. The detection of stolen vehicles can be done in an efficient manner by using the ANPR systems located in the highways. This paper presents a recognition method in which the vehicle plate image is obtained by the digital cameras and the image is processed to get the number plate information. A rear image of a vehicle is captured and processed using various algorithms. In this context, the number plate area is localized using a novel „feature-based number plate localization‟ method which consists of many algorithms. But our study mainly focusing on the two fast algorithms i.e., Edge Finding Method and Window Filtering Method for the better development of the number plate detection system
Introduction
Most of the number plate localization algorithms merge several procedures, resulting in long computational (and accordingly considerable execution) time (this may be reduced by applying less and simpler algorithms). The results are highly dependent on the image quality, since the reliability of the procedures severely degrades in the case of complex, noisy pictures that contain a lot of details. Unfortunately the various procedures barely offer remedy for this problem, precise camera adjustment is the only solution. This means that the car must be photographed in a way that the environment is excluded as possible and the size of the number plate is as big as possible. Adjustment of the size is especially difficult in the case of fast cars, since the optimum moment of exposure can hardly be guaranteed. Number Plate Localization on the Basis of Edge Finding: The algorithms rely on the observation that number plates usually appear as high contrast areas in the image (black-and-white or black-and-yellow). First, the original car image in color is converted to black and white image grayscale image as shown in figure
Number Plate Localization on the Basis of Window Filtering
The drawback of the above solution (Edge Finding Methodology) is that after the filtering also additional areas of high intensity appear besides the number plate. If the image contains a lot of details and edges (example: complex background) the further areas. As a result, the SFR curve exhibits a smaller increment at the number plate and the edges in the surrounding areas may sometimes be more dominantThe original image with complex Background is Filtered and the filtered image shows the High contrast regions apart from the number plate. The surroundings are unnecessarily included in the image which made the scene complex. We need to consider a window to exclude the surroundings from the image and concentrate on the actual image. For this we need to consider an appropriate window size. The window size is estimated on the basis of the expected size of the number plate. If the window is chosen to be as wide as the image, then the previously introduced algorithm is obtained, while too small window size leads to incorrect results. This latter algorithm reveals the number plate more effectively from its surroundings. The best result is obtained if the window size equals the width of the number plate, but smaller window dimensions provide fairly good values too. After determining the appropriate window size, we perform the sum of filtered rows and columns and the graph looks like this
Introduction to images
An image is a matrix with X rows and Y columns. It is represented as function say f(x, y) of intensity values for each color over a 2D plane. 2D points, pixel coordinates in an image, can be denoted using a pair of values. The image is stored as a small squared regions or number of picture elements called pixels as shown in the following figureIn digital image, pixels contain color value and each pixel uses 8 bits (0 to 7 bits). Most commonly, image has three types of representation gray scale image, Binary image and colored image as shown in figure 8 (b), ©, (d) respectively. Gray scale image, figure (b), calculates the intensity of light and it contains 8 bits (or one Byte or 256 values i.e. 28 = 256). Each pixel in the gray scale image represents one of the 256 values, in particular the value 0 represents black, 255 represents the white and the remaining values represents intermediate shades between black and white. The images with only two colors (black and white) are different to these gray scale images. Those two colored images are called binary images ©. So binary representation of the images does not contains shades between black and white. Color images, (d) are often built of several stacked color channels, each of them representing value levels of the given channel. For example, RGB images are composed of three independent channels for red, green and blue as primary color components. The color image contains 24 bits or 3 bytes and each byte has 256 values from 0 to 255.
Process of acquisition
Image acquisition is the process of obtaining an image from the camera. This is the first step of any vision based systems. In our current research we acquire the images using a digital camera placed by the road side facing towards the incoming vehicles .Here our aim is to get the frontal image of vehicles which contains license plate. The remaining stages of the system works in offline mode. Grayscale image: After acquiring the image, the very next step is to derive the gray scale image. Pseudo code to convert an image to a grayscale:
STEP1 : Load the image
STEP2 : Retrieve the properties of image like width, height and nchannels STEP3: Get the pointer to access image data
STEP4: For each height and for each width of the image, convert image to grayscale by calculating average of r,g,b channels of the imageconvert to grayscale manually
STEP5 : Display the image after converting to grayscale
Connected components
Connected components labeling scans an image and groups its pixels into components based on pixel connectivity, i.e. all pixels in a connected component share similar pixel intensity values and are in some way connected with each other. Once all groups have been determined, each pixel is labeled with a gray level or a color (color labeling) according to the component it was assigned to. After the Localization of the number plate of the vehicle involved, we need to recognize the number plate into a standard form. The vehicular number plates maybe of Non-standard forms and may vary in their fonts
Character Segmentation
Segmentation is one of the most important processes in the automatic number plate recognition, because all further steps rely on it. If the segmentation fails, a character can be improperly divided into two pieces, or two characters can be improperly merged together. We can use a horizontal projection of a number plate for the segmentation, or one of the more sophisticated methods, such as segmentation using the neural networks. In this segmentation we use two types of segmentation: 1. Horizontal segmentation 2. Vertical segmentation. First we have performed vertical segmentation on the number plate then the characters are vertically segmented. After performing vertical segmentation we have to perform horizontal segmentation by doing this we get character from the plate
Character Recognition
We have to recognize the characters we should perform feature extraction which is the basic concept to recognize the character. The feature extraction is a process of transformation of data from a bitmap representation into a form of descriptors, which are more suitable for computers. The recognition of character should be invariant towards the user font type, or deformations caused by a skew. In addition, all instances of the same character should have a similar description. A description of the character is a vector of numeral values, so called descriptors or patterns.
Conclusion
This paper presents a recognition method in which the vehicle plate image is obtained by the digital cameras and the image is processed to get the number plate information. A rear image of a vehicle is captured and processed using various algorithms. Further we are planning to study about the characteristics involved with the automatic number plate system for better performance