# Algorithm for License Plate Localization and Recognition for Tanzania Car Plate Numbers

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3 Firstly, gray level of all pixels is scaled into the range of 0 to 100 and compared with the original range 0 to 255; the character pixels and the background pixels are both weakened. Secondly, sorting all pixels by gray level in descending order and multiply the gray level of the top 20% pixels by Then most characters pixels are enhanced while background pixels keep weakened. 3.2 MATLAB Results The following figures were taken from MATLAB result report. Fig 4 (a) shows the original image and (b) the gray scale image. 3) After preprocessing and object enhanced algorithm Horizontal segmentation is done and vertical bounds are noted to segment the characters on the number plate of the image. 3. Results and discussion To test the proposed system experiments have been performed. The simulation process is carried out in MATLAB (R2010b) for the license plate localization and recognition. A set of 50 images were used for testing the proposed system. The images were taken from various environments. They are of different sizes and different colors. The plates were taken from complex images which contain different colors and several objects in the image. The images are taken from different inclined angles and distances relative to camera. One example which is successful for coloured method is given below the normal image is fed to the software to get the gray image where numbers or letters can be identified then license plate number is located and extracted. 3.1 How Matlab codes work First, the image is read from the directory with imread command; MATLAB stores the pixel values by 480 x 640 x 3 uint8 matrix. For the MATLAB implementation the resolution of the used images are 480 x 640. Since the algorithm depends on the intensity change in the image, binary image is needed. Binary image is generated by converting image to the gray scale first and then applying edge detection function. As a second step, with rgb2gray function; a new matrix 480 x 640 x 1 uint8 size is generated by taking the average of Red, Green, Blue pixel values. Then, for sobel edge detection edge (image, sobel) command is used. An intensity_change matrix is formed with size 480 x 1 by using the edge image. This matrix indicates the number of intensity change in a row; that is to say, the value of matrix for each row is increased when the side by side three pixel values are 0, 1, 0 or 1, 0, 1. For the next step, maximum value and the maximum index of the intensity_change matrix is found by max command. The maximum value is divided by two and taking maximum index as the middle, 81 row of matrix is compared with the half of maximum value. The adjacent rows greater than half value are the candidates of plate located rows. The height of the plate is calculated as approximately five times the difference of maximum row and minimum row. For the column location of numbered plate, this proportion is checked. While minimum and maximum column location greater than the proportion, the algorithm searches on the rows 1, 0, 1 sequence both from right and left side. Figure 4 (a) Figure 4 (b) Fig 5 (a) shows the sobel edge detected binary image and (b) the intensity change of the rows. The sobel edge detection threshold is used as MATLAB default. Figure 5 (a) Figure 5 (b) Fig 6 (a) shows the result of calculation of the rows which have greater value than half of maximum value of intensity variation and (b) the plate location on the image. The row and column location of the numbered plate is found. 14

5 Applications, ISSN: , Vol.1, No. 23, pp , [16] R. C. Gonzalez and R. E. Woods, Digital Image Processing, Pearson Education Asia, Author Profile Isack Emmanuel Bulugu received the B. Sc Electronic Science and Communication from University of Dar es salaam (UDSM), Dar es salaam, Tanzania in Presently a Masters Student of Signal and Information Processing in Tianjin University of Technology and Education (TUTE) Tianjin, P.R. China, Currently a Graduate Student Member of ilab sponsored and works at University of Dar es salaam as a Teaching assistant. His research interest includes embedded system and signal processing. 16

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