![](bg4.png)
We can also highlight that total of pass, extra and
Miss1 errors for CCT1 (954 + 193 + 16 = 1163)
was higher than CT130 (1026). This has proved that
RGB convolutions and Threshold method had its own
significant in choosing the appropriate winner clus-
ter. Additionally, CCT1 has successfully obtained
the highest license plate detection rate 96.44% while
CT130 achieved the second place with 94.17%.It can
be concluded that combination of RGB Convolution
with threshold one value techniques, can boost up the
license plate detection up to 96.44%.
From the above results, a few advantages and
disadvantages were notified with the proposed ap-
proaches, RGB convolutions and a new edge detector
with 128 grayscale offset that were applied. The ad-
vantages are;
i. Eventhough the original image of the back or
frontal car is having fusion problems, CCT1 can still
successfully detected the locations of the license plate
as shown in Figure 7.
ii.CCT1 can increase number of passing rate.
iii. CCT1 can increase number of extra blobs er-
rors.
iv.CCT1 can reduce number of missing Miss1,
Miss2, Miss≻2 blobs consisted in the winner clus-
ter(s). Besides that, those missing blobs are normally
at the beginning or ending.
The disadvantages are:
i.The passing rate for CCT1 were only slightly in-
creased compared to CT130. This is because CCT1
will consider all blobs in the image but not for CT130.
ii. RGB convolution with a new edge detector is
very time consuming because the calculations of get-
ting new grayscale output requires every pixel of the
original image to be analyzed.
iii. Quite often memory becomes leaking when us-
ing RGB convolution because it requires high storage.
As conclusion,CCT1 can boost up the detection rate
of license plates by suggestions below,
i. Instead of using fixed thresholding in CT130,
adaptive zoning thresholding can also help CCT1 to
improve its detection rate.
ii. RGB convolutions can apply other edge detec-
tors with 128 grayscale offset.
iii. Before applying RGB convoultions and Hori-
zontal and Vertical Projections, perhaps checking the
cluster’s original image using binary projections can
increase the whole performance.
iv. Incorparating uncertainty value while calculat-
ing maximum number of blobs and runlength of each
clusters may increase the LPSeeker’s performance.
This paper has generally discussed on concept of li-
cense plate recognition and segmentation techniques
which covers clustering, RGB convolutions and Run
Length Smoothing Algorithm. In conclusion, we
can conclude combination of RGB convolutions, a
new edge detector with 128 grayscale offset and Run
Figure 7: Fusion Images that has been successfully detected
by LPSeeker.
Length Smoothing Algorithm has significantly raised
detection rate of license plate’s location in the seg-
mentation phase.
Table 2: Detection rate for three experiments: Cluster with
Threshold Value 130, Cluster with RGB Convolution and
Cluster with RGB Convolution and Threshold Value 1.
Error Cluster and Cluster and
Threshold 130 RGB Convolve and
(CT130) Threshold 1(CCT1)
Type Total Percentage Total Percentage
Pass 947 76.68% 954 77.25%
Miss1 32 2.59% 193 15.63%
Miss2 47 3.81% 16 1.30%
Miss≻2 55 4.45% 16 1.30%
Extra 82 6.64% 12 0.97%
Fail 72 5.83% 44 3.56%
total 1235 100% 1237 100%
Total correct 1163 94.17% 1191 96.44%
Total incorrect 72 5.83% 44 3.56%
total 1235 100% 1235 100%
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