Vehicle License
Plate (VLP)
Recognition
System
By German H. Flores and Gurpal Bhoot
Agenda

Introduction

Goal and Motivation

Image Segmentation

Feature Extraction

Classification

Results/Conclusion

Future Work
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
Process Flow
•Ex: Separate LP
from car and
background as
well as
characters from
LP
•Extract features
that can be
used for
classification
•Ex: Area,
Perimeter,
Number of
Corners,
Contains Hole
Pattern Classification
•Locate objects
and boundaries
in images
Feature Extraction
Image Segmentation
Object Recognition Process
•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)
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
Image Segmentation
Binary Image
 Convert
image

the original image into a binary
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
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
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
Feature Extraction
 What
features are important for a
successful pattern classification?

Ex: Color, Area, Perimeter, mean, variance
 Character
Area
Recognition
Perimeter
Number of
Corners in
compressed
simple
image
Compressed
and
Perimeter of
Has Hole
Normalized
Contour
Character
Image
Number of
Corners in
Distance
compresse
Image
d full image
Feature Extraction
Area
Simple Compression
And Normalized Corners
Perimeter
Compressed and Normalized
Full Compression And
Perimeter of Contour
Normalized Corners
Feature Extraction
(http://www.leewardpro.com/articles/licplatefonts/font-penitentiary.html)
Characters that have holes
ABDOPQR0
689
Characters that do not have holes
CEFGHIJKL
MNSTUVWX
YZ123457
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 corner can be defined as the intersection of two edges
A new Corner Matching Algorithm Based on Gradient. (Yu, Haliyan.,., Ren Cuihua., and Qiao Xiaoling)
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
A
B
C
D
E
103
120
95
117
90
74
106
75
99
86
HAS
HOLES
1
1
0
1
0
Character Features Extracted
From Image
PERIMETER OF
CONTOUR
85
102
70
81
50
Number of Corners in
simple compression
63
51
63
43
36
Number of Corners in
full compression
202
262
255
270
438
Character Features
from Database
Correlation
Corr2()
Results
LICENSE PLATE
LICENSE PLATE CHARACTERS RECOGNIZED
3DDF536
--
D
5
3
EZEZBEH
E
2
E
Z
B
E
3HOS909
H
O
9
3
S
9
0
4HCF116
4
H
C
F
1
1
6
2LOX542
2
O
X
5
4
2
4FJF892
4
F
F
8
9
2
J
3TFB805
T
F
B
3
8
0
5
3WVD539
3
3
9
3GXP106
3
G
X
P
1
O
6
4EYB802
4
E
Y
B
8
0
2
4DNX245
---
4
D
N
X
2
4
5
4CGS613
---
C
G
S
6
1
3
---
3XHK859
3
X
H
X
8
5
9
3JXK363
X
K
6
3
Results
Results
Conclusion/Overview
Raw Image
Image
•License
Segmentation Plate
Letter
Segmentation
•Characters
Feature
Extraction
ABDOP
QR0689
CEFGHI
JKLMNS
TUVWXY
Z123457
•Area
•Perimeter
•Number of
Corners
Character
Feature
Database
•All the
characters
(A-Z) and
(0-9)
Classification
•Correlation
Bibliography