California Car License Plate Recognition System ZhengHui Hu Advisor: Dr. Kang.

California Car License Plate California Car License Plate Recognition SystemRecognition System

ZhengHui Hu

Advisor: Dr. Kang

12/11/2006 License Plate Recognition System 2

IntroductionIntroduction

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.


ApplicationsApplications

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


Technical IssuesTechnical Issues

Image Capturing– Vehicle speed– Lighting condition– Occlusion

Processing speed– Heavy traffic

Recognition accuracy– High correctness


Current StateCurrent 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


System ArchitectureSystem Architecture

Plate Region Segmentation– Locate plate region out of car

and/or background Character Segmentation

– Segment each character/number out of plate

Character Recognition– Recognize each character on

the plate– Similar to OCR process

Plate Segmentation Module

Character Segmentation Module

Character Recognition Module


Previous WorkPrevious Work

Most previous work are focused on the character segmentation and recognition process based on– Fuzzy algorithms– Template matching– Neural network


Step1 - Plate Region SegmentationStep1 - 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



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



Image Preprocessing

Filter using Color and Edge Information

Connected Components Analysis

Edge Information

Find Candidate Regions

Input Image

Feedback for more filtering


Input ImagesInput Images

Captured using a digital camera – Different distance– Different lighting

conditions– Different angles

Original size 2048X1536

Resized to 800X600 for faster process


Edge InformationEdge Information

Apply morphological operator to detect region of high change.

Plate character/numbers are among these


FilterFilter

Filter using Color and Edge Information– Use edge information

to find plate background color

– Filter image using plate background color


Connected Component AnalysisConnected Component Analysis

Find connected component and values– Width/Height ratio– Amount of edge pixels


Find CandidateFind Candidate

Plate has ratio between 1 and 3

Plate has highest or 2nd highest pixel density from edge image


Experiment Results 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


Experiment Results (Experiment Results (SpeedSpeed))

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


Step2 - Step2 - Character SegmentationCharacter 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


Step3 - Step3 - Character RecognitionCharacter Recognition

A process to recognize each character/number segmented

Challenges– Noise– Image scaling and distortion – Image corruption


Step3 - Step3 - Character RecognitionCharacter 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


ConclusionConclusion

Contributions – Algorithm for plate detection– Combination of back-propagation/simulated

annealing process in neural network trainingFuture Work

– Improve recognition ratio in step1 via feedback for filtering and better connected component analysis

– Finish Step3


ReferencesReferences

See Resources page in Website for full list of references

California Car License Plate Recognition System ZhengHui Hu Advisor: Dr. Kang.

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