ZhengHuiHu_presentation031907

8/2/2019 ZhengHuiHu_presentation031907

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California Car License Plate

Recognition System

ZhengHui Hu

Advisor: Dr. Kang


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Introduction

A License Plate Recognition System

(LPRS) is a system to automatically

detect, recognize and identify avehicle plate.

It involves low-level image

processing techniques with higherlevel artificial intelligence

techniques.


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Applications

Mainly for monitoring, surveillanceand security. For example,

Entrance/Exit monitoring for parking lotstructures

Part of surveillance system for gatedcommunities

Control gateways for vehicle passage

Security Systems for high traffic

Law Enforcement


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Challenges

Image Capturing Vehicle speed

Lighting condition

Occlusion

Processing speed

Heavy trafficRecognition accuracy

High correctness


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Recognition Stages

Plate Localization Locate plate region out of

car and/or background

Character Segmentation Segment each

character/number out ofplate

Character Recognition Recognize each character

on the plate

Similar to OCR process

Plate Localization

Character

Segmentation

Character Recognition


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Previous Work

Many difference solutions have

already been proposed for each

stage of recognitionPlate localization

Use edge statistics to locate the plate

Fuzzy clustering algorithms


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Previous Work

Character Segmentation Vertical/horizontal projection Adaptive Clustering

Optical Character Recognition Template matching

Neural network

Feature analysis


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Approach

Target: California State License

Plates The word California appears at the

top-center on the plate in red and italic. The plate number starts with a digit(0-9),

followed by three English characters (A-

Z) and three more digits (0-9). The plate background is a light shade of

gray while its characters are of dark

blueish color.


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Input Images

Captured using adigital camera Different distance

Different lightingconditions

Different angles

Original size2048X1536

Resized to 800X600for faster process


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Base Knowledge

No assumption on size nor the

possible location.

Helpful knowledge light background color and dark

foreground

rectangular shape with same width andheight proportion high in edge concentration


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Plate Localization

Noise Filtering andBrightness normalization

Extract Edge Information

Filter using Colorand Edge Information

Connected Component

Analysis

Candidate(s) Found

Continue to

Character Segmentation

> Filter thresholdFailed


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Extract Edge Information

Create a difference

image using

equationimg =threshold(close(src)-src)

High frequency

features areenhanced by the

operation


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Filtering

Select plate

background color

using two criteria Next to edge pixels

detected in

previous step

A light shade ofgray


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Connected Component

AnalysisCreate connected

components

Find candidateregions

High concentration

of edge pixels

Width/Height ratiosimilar to plate


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Candidate Regions

Multiple candidate

regions can be

found at this stage

All of them will be

submitted to next

stage


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Filter and Threshold

Find componentsof similar size

regionunprocessed ?

No

Count Components

Resizecomponents

7

Interpolate boxes

< 2> 2 and < 7

Crop Picture

Continue to Neural NetworkRecognition

Failed


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Filter and Threshold

Color filter the plate to remove foreignelements

Apply inverse binary threshold using thefollowing function

fimgij=255, if imgijthreshold

0, otherwise


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Character Extraction

Find a series of boxes with similar shapeand size. These are the individual

characters If number of boxes found is less than 7then an interpolation is performed


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Character Recognition

Two neural networks are used. One for recognition of digits (0-9) One for characters (A-Z)

Both networks were trained by using ahybrid method combining traditionalBack-propagation algorithm with a

Simulated Annealing process


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Artificial Neural Network

Artificial NeuralNetworks (ANN)

are modeled afterthe human brain Network of

processing units

called Neurons Good for solving

classificationproblems

InputLayer

HiddenLayer

OutputLayer


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Neurons

Neurons process information byreceiving and firing signal according to

internal function Two different types are used Threshold based step function

Sigmoid function

f=1, if wijthreshold0, otherwise

f=1

1et


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ANN Training

ANN must be trained by example beforeuse.

Supervised Training ANN receive set of Input data, and output ANN is adjusted according to the error

produced Repeat with different set of data

Back propagation (B-P) algorithm is aclassical supervised training method


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Limitation of B-P training

Same limitation ofany gradient

descent algorithm Lengthy flatplateau travel

Local minimatrap

Localmaximum

GlobalOptimum

FlatPlateau


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Simulated Annealing (SA)

Probabilistic heuristic for locating globaloptimum in large search space Invented

by Kirkpatrick, S., Gelatt, C.D., andVecchi, M.P. in 1983 Inspired by metallurgic annealing

process in which metal is cool down

gradually to get the best configuration Inner random selection allows it to

escape from local minima trapping


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Hybrid B-P SA Training

Train neighbors

Find neighbors

Create new ANN, astraining candidate

Train candidate

NN with least

error is new candidate

reduce T

End Training

< thresholdOR T > 0


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Experimental Results

Hardware used

Pentium 4 1.0 GHz processor and 512

MB of RAMImages

50 Images were acquired using a digital

cameraOriginal image size: 2048X1536

Test image size: 800X600


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Plate Localization


Total Images Plate located Failed to locate Success rate

50 48 2 96%

Total Images Character

segmented

Failed Success rate Cumulative success

rate

48 45 3 93.75% 90%


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Optical Character Recognition Training 5 images as basis for each digit Create 9 variations by adding noise, altering

column/rows, distortion, etc.. Recognition rate of digits

Recognition rate on training data 80%

Recognition on actual images extracted from LPRS 60%


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Experiment Results

Processing time

Average processing time from image

input to result: ~ 300ms


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Conclusion

Preprocessing

Proposed algorithm's performance are

satisfactoryNeural network training

New method combining Back-

Propagation algorithm with SimulatedAnnealing process


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Future Work

Extend current system to alsorecognize uncharacteristic plates andadditional character set.

Improve recognition ratio by usingalternative ANN configurations


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References

1.William K. Pratt, Digital Image Processing, Third Edition, JohnWiley & Sons, 20012.R. Parisi, E.D.Di Claudio, G.Lucarelli, and G. Orlandi, Car PlateRecognition by neural networks and image processing, Proceedingsof the 1998 IEEE International Symposium on Circuits and Systems,

(ISCAS '98).3.Leonard G. C. Hamey, Colin Priest, Automatic Number PlateRecognition for Australian Coditions, Proceedings of the DigitalImaging Computing: Techniques and Applications (DICTA 2005)4.Bai Hongliang, Liu Changping, A Hybrid License Plate ExtractionMethod Based on Edge Statistics and Morphology, Proceedings of the

17th

International Conference on Pattern Recognition(ICPR'04)5.Choudhury A. Rahman, Wael Badawy, Ahmad Radmanesh, A RealTime Vehicle's License Plate Recognition System, Proceedings of theIEEE Conference on Advanced Video and Signal Based Surveillance(AVSS'03)


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References

6.Takashi Naito, TOshihiko Tsukada, Keiichi Yamada, Kazuhiro Kozuka,Shin Yamamoto, Robust License-Plate Recognition Method forPassing Vehicles Under Outside Environment, IEEE Transactions onVehicular Technology, 20007.Rodolfo Zunino, Stefano Rovetta, Vector Quantization for License

Plate Location and Image coding, IEEE transactions on IndustrialElectronics, Vol 47, No. 1, 20008.Mi-Ae Ko, Young-Mo Kim, License Plate Surveillance System UsingWeighted Template Matching , Proceedings of the 32ndAppliedImagery Pattern Recognition Workshop (AIPR' 03)9.Feng Yang, Zheng Ma, Vehicle License Plate Location Based on

Histogram and Mathematical Morphology, Proceedings of the FourthIEEE Workshop on Automatic Identification Advanced Technologies(AutoID'05)10.Shyang-Lih Chang, Li-Shien Chen, Yun-Chung Chung, Sei-WanChen, Automatic License Plate Recognition, IEEE transactions onIntelligent Transportation Systems, Vol 5, No. 1, 2004


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References

11.Juntanasub, R., Sureerattanan, N., Car license plate recognitionthrough Hausdorff distance technique, Proceedings of the 17th IEEEInternational Conference on Tools with Artificial Intelligence, (ICTAI' 05)12.Timothy Masters, Advanced Algorithms for neural networks?, JohnWiley & Sons, 1995

13.Cornelius T. Leondes (Editor), ?Algorithms and Architectures(Neural Networks Systems Techniques and Applications), AcademicPress, 199814.David E. Rumelhart, Geoffrey E. Hinton & Ronald J. Williams,Learning representations by back-propagating errors, Nature 323, 533- 536 (09 October 1986)

15.Kirkpatrick, S., Gelatt, C.D., and Vecchi, M.P., Optimization bySimulated Annealing, Science, Volume 220, Number 4598, 13 May1983, pp. 671680.

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