Vehicle License Plate (VLP) Recognition System By German H. Flores and Gurpal Bhoot

 Introduction  Goal and Motivation  Image Segmentation  Feature Extraction  Classification  Results/Conclusion  Future Work Agenda

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

Video

Image Segmentation Locate objects and boundaries in images Ex: Separate LP from car and background as well as characters from LP Feature Extraction Extract features that can be used for classification Ex: Area, Perimeter, Number of Corners, Contains Hole Pattern Classification Take the features extracted from the image and use them to automatically classify image objects Ex: Classify either as letters (A-Z) and/or numbers (0-9) Object Recognition Process Process Flow

 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 Assumptions

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

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

 What features are important for a successful pattern classification?  Ex: Color, Area, Perimeter, mean, variance  Character Recognition Area Number of Corners in compressed simple image PerimeterHas Hole Number of Corners in compresse d full image Perimeter of Contour Distance Image Compressed and Normalized Character Image Feature Extraction

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

( Characters that have holes Characters that do not have holes A B D O P Q R C E F G H I J K L M N S T U V W X Y Z 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 Feature Extraction

 Harris Corner Detection A new Corner Matching Algorithm Based on Gradient. (Yu, Haliyan.,., Ren Cuihua., and Qiao Xiaoling) A corner can be defined as the intersection of two edges Feature Extraction

1. Compute X and Y derivatives of the grayscale image Gx Gy 2. Compute products of derivatives 3. Define at each pixel (x,y), the matrix 4. Compute the response at each pixel 5. Threshold on Value R 0s or negative numbers are the corners Feature Extraction

CHARACTERAREAPERIMETER HAS HOLES PERIMETER OF CONTOUR Number of Corners in simple compression Number of Corners in full compression A B C D E Character Features Extracted From Image Character Features from Database Correlation Corr2() Feature Extraction

LICENSE PLATELICENSE PLATE CHARACTERS RECOGNIZED 3DDF536--D53 EZEZBEHE2EZBE 3HOS909HO93S90 4HCF1164HCF116 2LOX5422OX542 4FJF8924FF892J 3TFB805TFB3805 3WVD GXP1063GXP1O6 4EYB8024EYB802 4DNX DNX245 4CGS613---CGS613 3XHK8593XHX859 3JXK363XK63 Results

Raw Image Image Segmentation License Plate Letter Segmentation Characters Feature Extraction Area Perimeter Number of Corners Character Feature Database All the characters (A-Z) and (0-9) Classification Correlation A B D O P Q R C E F G H I J K L M N S T U V W X Y Z Conclusion/Overview

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