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

Imag

e Se

gmen

tatio

n • Locate objects and boundaries in images

• Ex: Separate LP from car and background as well as characters from LP

Feat

ure

Extra

ctio

n

• Extract features that can be used for classification

• Ex: Area, Perimeter, Number of Corners, Contains Hole

Patte

rn

Clas

sifica

tion• 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

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

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

Perimeter

Has 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 ContourSimple Compression

And Normalized Corners Full Compression AndNormalized Corners

Compressed and Normalized

Feature Extraction

(http://www.leewardpro.com/articles/licplatefonts/font-penitentiary.html)

Characters that have holes

Characters that do not have holes

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

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

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

CHARACTER AREA PERIMETER HAS HOLES

PERIMETER OF CONTOUR

Number of Corners in simple compression

Number of Corners in full compression

A 103 74 1 85 63 202B 120 106 1 102 51 262C 95 75 0 70 63 255D 117 99 1 81 43 270E 90 86 0 50 36 438

Character Features Extracted From Image

Character Features from Database

CorrelationCorr2()

Feature Extraction

LICENSE PLATE

LICENSE PLATE CHARACTERS RECOGNIZED

3DDF536 -- D 5 3EZEZBEH E 2 E Z B E3HOS909 H O 9 3 S 9 04HCF116 4 H C F 1 1 62LOX542 2 O X 5 4 24FJF892 4 F F 8 9 2 J3TFB805 T F B 3 8 0 53WVD539 3 3 93GXP106 3 G X P 1 O 64EYB802 4 E Y B 8 0 24DNX245 --- 4 D N X 2 4 54CGS613 --- C G S 6 1 3 ---3XHK859 3 X H X 8 5 93JXK363 X K 6 3

Results

Results

Results

Raw Image

Image Segmentatio

n•License Plate

Letter Segmentatio

n•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 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

Conclusion/Overview

Bibliography