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This is an archived project. Repository and other project resources are read-only.
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Taddeüs Kroes
licenseplates
Commits
a7c85d52
Commit
a7c85d52
authored
13 years ago
by
Jayke Meijer
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Changed results of grid search.
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365deb2d
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docs/report.tex
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View file @
a7c85d52
...
...
@@ -388,7 +388,7 @@ Performs a grid-search to find the optimal value for \texttt{c} and
optimal classifier is saved in
\emph
{
data/classifier
\_\{
BLUR
\_
SCALE
\}\_\{
NEIGBOURS
\}
.dat
}
, and the accuracy
scores are saved in
\emph
{
results/result
s
\_\{
BLUR
\_
SCALE
\}\_\{
NEIGBOURS
\}
.txt
}
.
\emph
{
results/result
\_\{
BLUR
\_
SCALE
\}\_\{
NEIGBOURS
\}
.txt
}
.
Like
\texttt
{
create
\_
classifier.py
}
, the script ensures that the required
character object files exist first.
...
...
@@ -445,7 +445,7 @@ find this parameter, we tested a few values, by trying them and checking the
results. It turned out that the best value was
$
\sigma
=
1
.
4
$
.
Theoretically, this can be explained as follows. The filter has width of
$
6
*
\sigma
=
6
*
1
.
4
=
8
.
4
$
pixels. The width of a `stroke' in a character is,
$
6
*
\sigma
=
6
*
1
.
6
=
9
.
6
$
pixels. The width of a `stroke' in a character is,
after our resize operations, around 8 pixels. This means, our filter `matches'
the smallest detail size we want to be able to see, so everything that is
smaller is properly suppressed, yet it retains the details we do want to keep,
...
...
@@ -558,11 +558,11 @@ According to Wikipedia \cite{wikiplate}, commercial license plate recognition
that are currently on the market software score about
$
90
\%
$
to
$
94
\%
$
, under
optimal conditions and with modern equipment.
Our program scores an average of
$
93
.
2
\%
$
. However, this is for a single
Our program scores an average of
$
93
.
6
\%
$
. However, this is for a single
character. That means that a full license plate should theoretically
get a score of
$
0
.
93
2
^
6
=
0
.
6
55
$
, so
$
6
5
.
5
\%
$
. That is not particularly
get a score of
$
0
.
93
6
^
6
=
0
.
6
72
$
, so
$
6
7
.
2
\%
$
. That is not particularly
good compared to the commercial ones. However, our focus was on getting
good scores per character. For us,
$
93
.
2
\%
$
is a very satisfying result.
good scores per character. For us,
$
93
.
6
\%
$
is a very satisfying result.
\subsubsection*
{
Faulty classified characters
}
...
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