Azhar Anwar5102316A2.ppt

1、Vehicle License Plate Identification and Recognition,Azhar Anwar 5102316 A2,CONTENTS,Introduction Survey Methodology References,INTRODUCTION,The problem of vehicle license plate recognition is an interesting one and over the years has attracted a plethora of researchers and computer vision experts.

2、The applications of such a system are vast and can range from parking lot security to traffic management.License Plate Recognition is an effective form of AVI systems. In this study, a smart and simple algorithm is presented for vehicles license plate recognition system.Nowadays vehicles play a very

3、 big role in transportation. Also the use of vehicles has been increasing because of population growth and human needs in recent years. Therefore, control of vehicles is becoming a big problem and much more difficult to solve .,LITERARY SURVEY,PAPERS REFFERED“Automatic Vehicle Identification by Plat

4、e Recognition”Proceedings of world academy of science,engineering and technology volume 9 november 2005, Serkan Ozbay, and Ergun Ercelebi“Vehicle License Plate Identification & Recognition”Sanjay Goel, Siddharth Batra, et alJaypee Institute of Information Technology, India,Vehicle License Plate Iden

5、tification & Recognition,The algorithm proposed in this paper is designed to recognize license plates of vehicles automatically. Input of the system is the image of a vehicle captured by a camera The captured image taken from 4-5 meters away is processed through the license plate extractor with givi

6、ng its output to segmentation part. Segmentation part separates the characters individually. And finally recognition part recognizes the characters giving the result as the plate number,CONTROL ALGORITHM,ALGORITHM,Plate region extraction is the first stage in this algorithm. Image captured from the

7、camera is first converted to the binary image consisting of only 1s and 0s (only black and white). To find the plate region, firstly smearing algorithm is used. Smearing is a method for the extraction of text areas on a mixed image. With the smearing algorithm, the image is processed along vertical

8、and horizontal runs (scan-lines). . If number of white pixels 100 ; pixels become black Else ; no changeAfter smearing, a morphological operation, dilation, is applied to the image for specifying the plate location In the segmentation of plate characters, license plate is segmented into its constitu

9、ent parts obtaining the characters individually.,( a ) Captured image,( b ) Binarized image,( a ) Plate region,( b ) Image involving only plate,In this paper, the presented application software is designed for the recognition of car license plate. Firstly we extracted the plate location, then we sep

10、arated the plate characters individually by segmentation and finally applied template matching with the use of correlation for recognition of plate characters.Finally it is proved to be %97.6 for the extraction of plate region, %96 for the segmentation of the characters and %98.8 for the recognition

11、 unit accurate, giving the overall system performance %92.57 recognition rates.,METHODOLOGY,. PLATE REGION EXTRACTION,Plate region extraction is the first stage in this algorithm. Image captured from the camera is first converted to the binary image consisting of only 1s and 0s (only black and white

12、). by thresholding the pixel values of 0 (black) for all pixels in the input image with luminance less than threshold value and 1 (white) for all other pixels. 2. The binarized image is then processed using some methods. To find the plate region, firstly smearing algorithm is used. Smearing is a met

13、hod for the extraction of text areas on a mixed image. With the smearing algorithm, the image is processed along vertical and horizontal runs (scan-lines). If the number of white pixels is less than a desired threshold or greater than any other desired threshold, white pixels are converted to black.

14、,SEGMENTATION,In the segmentation of plate characters, license plate is segmented into its constituent parts obtaining the characters individually. Firstly, image is filtered for enhancing the image.Then dilation operation is applied to the image for separating the characters from each other if the

15、characters are close to each other. After this operation, horizontal and vertical smearing are applied for finding the character regions.The next step is to cut the plate characters. It is done by finding starting and end points of characters in horizontal direction.,Character Recognition,Before rec

16、ognition algorithm, the characters are normalized. Normalization is to refine the characters into a block containing no extra white spaces (pixels) in all the four sides of the characters.Fitting approach is necessary for template matching. For matching the characters with the database, input images

17、 must be equal-sized with the database characters.To measure the similarity and find the best match, a statistical method correlation is used. Correlation is an effective technique for image recognition which was developed by Horowitz.Cross-correlation function (CCF) is a measure of the similarities

18、 or shared properties between two signals. Since there are two signals as unknown input image and known database image in this system, cross-correlation is used.,Because of the similarities of some characters, there may be some errors during recognition. The confused characters mainly are B and 8, E

19、 and F, D and O, S and 5, Z and 2. To increase the recognition rate, some criteria tests are used in the system for the confused characters defining the special features of the characters. With these features of characters and applied tests during recognition algorithm, recognition rate is increased

20、 with the minimum error.,REFERENCES,“Automatic Vehicle Identification by Plate Recognition”Proceedings of world academy of science,engineering and technology volume 9 november 2005, Serkan Ozbay, and Ergun Ercelebi“Vehicle License Plate Identification & Recognition”Sanjay Goel, Siddharth Batra, et alJaypee Institute of Information Technology, India,