Vehicle License Plate (VLP) Recognition System

1. Vehicle License Plate (VLP) Recognition System By German H. Flores and GurpalBhoot

2. Agenda • Introduction • Goal and Motivation • Image Segmentation • Feature Extraction • Classification • Results/Conclusion • Future Work

3. Introduction • Technological advancements in both software and hardware • Better ways to capture, edit and analyze images • Safety and security of pedestrians and people in motorized vehicles • The large number of cars on the roads has increased the probability of an accident occurring • With a VLP system, the owner of a car can be easily identified and held responsible for their actions

4. Video

5. Object Recognition Process Process Flow

6. Assumptions • Ideal lighting Conditions • Non-white car • License Plate is in the same region • License Plates are similar sizes • Only California license plates after 1987 • License Plates must be white with dark characters • Upper case letter O and 0 are the same

7. Binary Image Image Segmentation • Convert the original image into a binary image • Threshold was chosen through testing Binary Image Resize Image • Shrink the image • Cut out the background • Leave only part of the image where license plate is most likely to appear

8. Image Segmentation Windowing Method • Windowing Method used to find the license plate from the binary image • Send a window (m X n) through binary image, pixel by pixel Resized Binary Image

9. Image Segmentation Windowing Method • Find the license plate by number of white pixels • Below is the resulting image from applying the Window Method Final Binary Image

10. Image Segmentation Connected Component Algorithm • Used for separating license plate from the image • Finds the different objects • Finds the license plate by size and shape Extracted License Plate • Then used for separating the letters and numbers • Finds each character and extracts them one by one

11. Image Segmentation

12. Feature Extraction • What features are important for a successful pattern classification? • Ex: Color, Area, Perimeter, mean, variance • Character Recognition

13. Feature Extraction Area Perimeter Compressed and Normalized Simple Compression And Normalized Corners Full Compression And Normalized Corners Perimeter of Contour

14. Feature Extraction (http://www.leewardpro.com/articles/licplatefonts/font-penitentiary.html) Characters that have holes • Features: • Area • Perimeter • Perimeter of Contour • Number of Corners in simple • compressed Image • Number of Corners in full • compressed Image • Distance Image • Normalized Character Image A B D O P Q R 0 6 8 9 Characters that do not have holes C E F G H I J K L M N S T U V W X Y Z 1 2 3 4 5 7

15. Feature Extraction A corner can be defined as the intersection of two edges • Harris Corner Detection A new Corner Matching Algorithm Based on Gradient. (Yu, Haliyan.,., RenCuihua., and QiaoXiaoling)

16. Feature Extraction

17. Feature Extraction • Compute X and Y derivatives of the grayscale image GxGy • Compute products of derivatives • Define at each pixel (x,y), the matrix • Compute the response at each pixel • Threshold on Value R 0s or negative numbers are the corners

18. Feature Extraction Character Features Extracted From Image Character Features from Database Correlation Corr2()

19. Results

20. Results

21. Results

22. Conclusion/Overview A B D O P Q R 0 6 8 9 C E F G H I J K L M N S T U V W X Y Z 1 2 3 4 5 7

23. Bibliography