Development of a License
Plate Number Recognition
System Incorporating LowResolution Cameras

System Image
Enforcement Cameras
Vehicle Image

Recognition
ResultRoad Side Equipment(Radio equipment)

KENTA NAKAO*1

KIICHI SUGIMOTO*1

MAYUMI SAITOH*1

TAKUMA OKAZAKI*2

A multi-lane free ﬂow (MLFF) toll collection system installed on a simpliﬁed gantry requires compact cameras for supervising
enforcement. Because these compact cameras have low image resolution, it is also necessary to develop vehicle license plate
recognition technology that uses dynamic image processing. Mitsubishi Heavy Industries, Ltd. (MHI) has developed threetechnologies based on the conventional license plate recognition system using still images; these technologies improve image quality,process plural images of a single vehicle, and utilize a reference database. Laboratory evaluation tests have veriﬁed that even a lowresolution camera system can successfully recognize license plate numbers at a rate of 95% or better, comparable to results from theconventional still image system. MHI is enhancing system robustness to enable application of these technologies to actual products.

record images of the license plates of noncompliant vehicles(such as those lacking an in-vehicle unit).
T hese photog raphs a re t ra nsfer red to t he cent ra lcomputer system, where the built-in software programfor vehicle license plate recognition identifies the vehicle
numbers (Fig. 1).
MHI’s vehicle license plate recognition systems havebeen in revenue service for more than 20 years and produce
a highly successful recognition rate of 95% or higher.

1. Introduction
MHI has developed vehicle toll collect ion systems,
i nclud i ng ele c t ron ic road pr ic i ng ( E R P ) s yst ems i n
Singapore and electronic toll collection (ETC) systems inJapan, where toll collection is controlled by communications
between antennas installed on roadside gantries (or frames)
and within in-vehicle units.1
Demand for multi-lane free flow (MLFF) toll collection
systems using simplified gantries is increasing worldwide,in order to reduce construction costs and preserve favorable
scenery.
Convent iona l toll collect ion systems super v ise
enforcement using large, high-resolution cameras that
produce high-quality still images to recognize vehicle license
plate numbers automatically.
The simpliﬁed gantries used in the MLFF toll collection
system require the supervision cameras to be more compact
than those used in conventional systems. Because compact
cameras produce lower image resolution, conventional
license plate recognition systems are unable to achieve therequired rate of successful recognition.
This paper presents novel image processing technologies
that can achieve rates of successful recognition equivalent
to those of conventional systems, even when using lowresolution supervision cameras.

Data entry of vehicle image

Plate search

Converting plate into binary data

Character recognition

Result of recognizing vehicle license plate
Fig. 1 Basic ﬂow of the vehicle license plate recognition
process

2. Recognition of vehicle license plate numbers

3. Technical problems

M H I has appl ied vehicle l icense plate recog n it ion
technologies to components of vehicle classiﬁcation units andto identify toll-evading vehicles. For example, Singapore’s
ER P system identifies the license plate numbers of tollevading vehicles by using cameras installed on gantries toSingapore’s ER P systems use large, high-resolution
supervision cameras. They record one still image of eachpassing vehicle, which is sufficient because the cameras
produce high quality, large-size images.
Compact cameras suitable for mounting on simplified*1 Takasago Research & Development Center, Technical Headquarters*2 Kobe Shipyard & Machinery Works

19

Mitsubishi Heavy Industries, Ltd.
Technical Review Vol. 45 No. 3 (Sep. 2008)

gantries would produce lower resolution images than thecurrent ER P system, causing concerns about degradedrecognition accuracy.
Recent improvement in computer per for ma nce hasenabled these compact, low-resolution cameras to processeach vehicle using motion images. This is a distinct beneﬁt,
but requires the technology to process multiple images.
Vehicle license plate recognition systems using compact,
low-resolution supervision cameras therefore face two main
problems:(1) low image resolution, and(2) processing plural images for one vehicle.
3.1 Low image resolution
Degraded resolution tends to result in images with blurredand defaced character strokes.
The low-power lighting device associated with compact
imaging devices also poses a problem of low brightness due
to lack of light (Fig. 2).
3.2 Processing plural images for one vehicle
A mot ion i ma ge of one veh icle consist s of plu r a l ,continuous images, making it possible to obtain the vehicle
license plate number from each image.
A l l t he i ma ges processed must produce t he s a merecognition result. If (a) two or more different results are

obtained from the images of a single vehicle, or (b) eachof the recognition results includes one or more illegible
characters, an additional process is required to identify thecorrect result (Fig. 3).

4. Features of the developed technology
4.1 Image processing algorithm compatible with lowresolution images
To solve the problems associated with low image quality
discussed in section 3.1, MHI has developed three kindsof image processing algorithms based on the conventional
vehicle license plate recognition algorithm:(1) plate extraction process,(2) contrast improvement process, and(3) character isolation process.
(1) Plate extraction process
A lack of sharpness in a license plate image may resultin a low-resolution or a low-contrast image, causing thesystem to select noise components erroneously as part ofthe plate data.
A n image sharpening process was added to help
achieve consistent plate extraction.
This image sharpening process is a combination of anoise filter and a sharpening process, improving image
sharpness while suppressing noise.
A n enla rgement process was also int roduced toprevent an increase in blurred or defaced characters due
to low resolution; this process incorporates the gradientin peripheral brightness (Fig. 4).

Image

CameraLarge,
high-resolution camera

Compact,
low-resolution camera

Contrast

Good (bright)Poor (dark)

Sharpness

Good (sharp characters)

Poor (blurred,defaced characters)

Characterseparation

Good (characterswell separated)

Poor (characters
poorly separated)

Sharpening
and(a) Low-resolution image enlarging(b) Processed image

Fig. 4 Effect of sharpening and enlarging images

Fig. 2 Technical problems due to degraded image quality

Same recognition results
for all(a) Two or more differing
recognition results(b) One or more unidentified charactersin each recognition resultt

Recognition result:
ABC123

Recognition result:
AB?12?

Recognition result:
AB?12?

t+1Recognition result:
ABC123

Recognition result:
ABC123

Recognition result:
A?C12?

t+2

Recognition result:
ABC123

Recognition result:
ABD123

Recognition result:
ABC?23t+3Recognition result:
ABC123

Recognition result:
A8C123

Recognition result:?B?1?3Singling out correct recognition
result is required.

Recognizing unidentified charactersis required.

Time

Photographs

ProblemFig. 3 Problems with recognition results using motion images

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Technical Review Vol. 45 No. 3 (Sep. 2008)20

(a) Sharpened,enlarged image(3) Character isolation process
Degradation in image quality can cause blurred or
defaced characters to merge. The enlargement solution
described above cannot solve this particular problem
because enlargement will not separate characters if they
are connected in the original image.
On the basis of st at ist ical in for mat ion obt a ined
mainly from the brightness histogram, MHI developed a
new process by which minute brightness changes can be
distinguished within the clearances between characters,
enabl i ng ident i f icat ion of bounda r ies bet ween
characters.
4.2 Algorithm utilizing plural recognition results
To solve the problem discussed in section 3.2, MHI further
developed a process to integrate the plural recognition
results obtained from one vehicle (Fig. 6).
The process first scores each character based on thecharacter recognition results. Score points are a numericalexpression of the likeliness between each character and theregistered character templates. Higher score points indicate
higher accuracy of character recognition.
Next, score points are allocated to each character in order(i.e., ﬁrst character, second character, and so forth).
Finally, the character with the highest allocated score
total points is selected as the ﬁnal recognition result.

(c) Image withcharacters highlighted(b) Image throughhigh-pass filter
Fig. 5 Highlighting characters using high-frequency image
superimposition(2) Contrast improvement process
A low-contrast image due to lack of light makes it
difﬁcult to accurately convert characters into binary data.
Therefore, MHI developed a pre-processing algorithm.
This algorithm analyzes statistic brightness information
with respect to the extracted plate and judges the contrast
quality, which in turn improves the brightness of eachpixel and automatically produces an optimal contrast.
The contrast-improved plate image goes through ahigh-pass ﬁlter to highlight the frequency components ofthe characters. The highlighted image is superimposedon t he or i g i na l i ma ge t o i nc rea s e t he br ight nes s
difference between the characters and the background(Fig. 5).

Actual number :ABC

Recognition result 1 :ABD

Recognition result 2 :B?CRecognition result 3 :ABC

4.3 Improving recognition accuracy utilizing adatabase
To f ur ther improve the accuracy of vehicle license
plate recognition, MHI developed a recognition accuracy(1) Obtaining plural recognition results

Score point

ABD

965

1st character 2nd character 3rd character
“A”

9.0

“B”

6.0

7.0
6.0

5.0

“D”

5.0

“C”

7.0

Recognition result 1 :Score point

ABD965

Recognition result 2 :Score point

B?C607

Recognition result 3 :Score point

ABC755(2) Score point depending on
character recognition results

Maximum score point,
1st character
Maximum score point,
2nd character
Maximum score point,
3rd character

Character“A”( 9+7=16 )“B”( 6+5=11 )“C”( 7+5=12 )(4) Selecting maximum point character at
each character position

5.0(3) Allocating recognition results toeach character position

Final recognition result : “A B C”

Fig. 6 Integration process of plural recognition results

Mitsubishi Heavy Industries, Ltd.
Technical Review Vol. 45 No. 3 (Sep. 2008)21

Time : tCommunications
log at time t

HBC123Time : t
BDC345 GHS785
OCK495 KM432
ABC123 WCK090
ICM32
LOP257
BBZ221 ZO194
PPQ878 WWY234

Number recognition result

ABC123
Estimated result

Communications
log at time t

Number recognition result

Reference

HBC123

Reference

BDC345 GHS785
OCK495 KM432
ABC123 BBC123
ICM32
CCC123
BBZ221 ZO194
PPQ878 WWY234

Order Class Evaluation
0.61
H0.52
A
0.43
B

ABC123

Communications log database

Communications log database

Estimated result(a) Reference with database to improve probabilityof number recognition result(b) Utilizing character recognition results
if similar numbers exist

Fig. 7 Reference with database

improvement technology that utilizes a database (Fig. 7).
A central computer system stores the database for a tollcollection system, including a communications log with invehicle units and registered vehicle license plates.
A recognition result of a vehicle license plate proves to becorrect if it agrees with one of the registered vehicle license
plates recorded in the database at that particular time.
Even if some characters are unidentified as a result of
the recognition process, the communications log in thedatabase can identify a vehicle that is registered with similar
characters, producing highly accurate recognition results.
However, the recog nit ion process may fa il i f ma ny
c h a r a c t e r s a r e u n i de nt i f i a bl e o r i f a s e a r c h of t h ecommunications log results in t wo or more vehicles ascandidates.
To cope with this problem, MHI developed a databasesearch technique that incorporates character recognition
results and a matching evaluation process.

using a number of vehicle images.
Vehicles were photographed using low-resolution cameras.
The images were treated to include noise, reduced contrast,lack of focus, and other defects to simulate the disturbing
inﬂuence of direct sunlight and shadows caused by changes
in climate, dirt on license plates, and deformation of licenseplates.
The evaluation tests revealed that the system successfullyrecognized vehicle license plates at an accuracy of 95% orhigher. The results of this novel process (patent pending)are as accurate as those of systems using conventional highresolution images (95%; see Fig. 8).

6. Conclusion
The MLFF toll collection system enables vehicles tomove freely, without necessitating the stops required attollgates. Demand for this type of system is expected toincrease due to its ability to reduce traffic jams and fuelconsumption.
M H I ’s novel system is based on t he compact , lowresolution cameras that are used in the MLFF toll collection
system. Its performance has proven to be equal to or better

5. Laboratory test evaluation

Recognition rate (%)

The performance of the novel license plate recognition
process incorporating low-resolution images was evaluated100

Process 1: Image processing algorithm
compatible with low-resolution
imagesProcess 2: Processing algorithm using
plural recognition results
Process 3: Improving recognition
accuracy utilizing database

95908580nenvoCal *1)(ntioses + 2
oc 1

ss 1

Preoc

Pr

(*1) Recognition rate by conventionallyprocessing low-resolution images

ses +3oc + 2rP 1

Fig. 8 Results of performance evaluation of developed technologies

Mitsubishi Heavy Industries, Ltd.
Technical Review Vol. 45 No. 3 (Sep. 2008)22

than results f rom conventional systems that use highresolution images.
T h is development c a n of fer M L F F t ol l c ol le c t ion
systems highly accurate enforcement with ﬂexible revenue
operation.
To ensure that the system becomes even more robust
for use in actual systems, MHI will continue to improve theprocess by evaluating more image data.

Kenta Nakao

Kiichi Sugimoto

Mayumi Saitoh

References
1. Hashimoto, H, et. al., Multi-Lane Electronic Road Pricing System
in Singapore, Mitsubishi Juko Giho Vol.36 No.1 (1999) p.50
2. Miyamoto, K . et al., Character Recognition for Tra f f ic andTransportation System, Mitsubishi Juko Giho Vol.33 No.6 (1996)
p.404

Takuma Okazaki

Mitsubishi Heavy Industries, Ltd.
Technical Review Vol. 45 No. 3 (Sep. 2008)23