California Car License Plate
Recognition System
ZhengHui Hu
Advisor: Dr. Kang
Introduction

A License Plate Recognition System
(LPRS) is a system to automatically
detect, recognize and identify a vehicle
plate.
 It involves low-level image processing
techniques with higher level artificial
intelligence techniques.
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Applications

Mainly for monitoring, surveillance and
security. For example,
– Entrance/Exit monitoring for parking lot
structures
– Part of surveillance system for gated
communities
– Control gateways for vehicle passage
– Security Systems for high traffic

Law Enforcement
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Technical Issues

Image Capturing
– Vehicle speed
– Lighting condition
– Occlusion

Processing speed
– Heavy traffic

Recognition accuracy
– High correctness
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Current State

There are many companies, especially in
Europe, that developed this type of
system commercially
 There are many research trying to
improve accuracy and speed
performance
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System Architecture
Plate Segmentation
Module

Plate Region Segmentation
– Locate plate region out of car
and/or background

– Segment each
Character
Segmentation Module
character/number out of plate

Character Recognition
Module
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Character Segmentation
Character Recognition
– Recognize each character on
the plate
– Similar to OCR process
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Previous Work

Most previous work are focused on the
character segmentation and recognition
process based on
– Fuzzy algorithms
– Template matching
– Neural network
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Step1 - Plate Region Segmentation

Goal
– Locate license plate in an image

Target image group
– California Car License Plates (regular ones)

Challenges
– Location: plate regions at random place
– Size: vehicle distance from the camera affect plate size
– Color: affected by lighting conditions (day/night/shadow)
– Skew/distortion: images can be taken from different angles
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Step1 - Plate Region Segmentation

Helpful information
– All License Plate have same shape
– Known background/foreground colors
 Light background color
 Bluish foreground color
– numbers and characters
– Color distribution in a rectangular plate
region
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Step1 - Plate Region Segmentation
Input Image
Image Preprocessing
Edge Information
Filter using Color and
Edge Information
Connected Components
Analysis
Feedback for
more filtering
Find Candidate Regions
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Input Images

Captured using a
digital camera
– Different distance
– Different lighting
conditions
– Different angles

Original size
2048X1536
 Resized to 800X600
for faster process
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Edge Information

Apply morphological
operator to detect
region of high
change.
 Plate
character/numbers
are among these
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Filter

Filter using Color
and Edge
Information
– Use edge information
to find plate
background color
– Filter image using
plate background
color
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Connected Component Analysis

Find connected
component and
values
– Width/Height ratio
– Amount of edge pixels
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Find Candidate

Plate has ratio
between 1 and 3
 Plate has highest or
2nd highest pixel
density from edge
image
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Experiment Results

Total Pictures Tested: 43
– Region found: 38
– Region not found: 5
– Success rate: 88%

Error classification
– Filtering process chopped out part of plate
– Fail to identify correct candidate region
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Experiment Results (Speed)

Machine Used for Testing: Pentium 4-M
1.70Ghz, 256 MB RAM
– For images 800X600, the processing time is
150 ~ 190 ms
– For original size image 2048X1536,
processing time is around 1 sec
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Step2 - Character Segmentation

Segment each character/number out of
the plate detected by previous module
 Challenges
– Rectangle segmented might contain more
than just the plate
– Plate might contain some things other than
number/characters

Still under development
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Step3 - Character Recognition

A process to recognize each
character/number segmented
 Challenges
– Noise
– Image scaling and distortion
– Image corruption
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Step3 - Character Recognition

Our approach
– Artificial Neural networks are used to
recognize characters and digits
– During training process, simulated annealing
process was added to the back propagation
training to avoid the problem of local
minimal

Still under development
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Conclusion

Contributions
– Algorithm for plate detection
– Combination of back-propagation/simulated
annealing process in neural network training

Future Work
– Improve recognition ratio in step1 via
feedback for filtering and better connected
component analysis
– Finish Step3
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References

See Resources page in Website for full list
of references
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