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Taddeüs Kroes
licenseplates
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b851b4a2
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b851b4a2
authored
13 years ago
by
Jayke Meijer
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Added whining about crappy dataset.
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b851b4a2
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@@ -543,12 +543,11 @@ XML file. This creates a large diagonal line in the image, which explains why
this can be classified as a 7. The same has happened with a 'T', which is also
marked as 7.
Other strange matches include a 'Z' as a 9, but
this character has a lot of noise surrounding it, which makes classification
harder, and a 3 that is classified as 9, where the exact opposite is the case.
This plate has no noise, due to which the background is a large area of equal
color. This might cause the classification to focus more on this than on the
actual character.
Other strange matches include a 'Z' as a 9, but this character has a lot of
noise surrounding it, which makes classification harder, and a 3 that is
classified as 9, where the exact opposite is the case. This plate has no noise,
due to which the background is a large area of equal color. This might cause
the classification to focus more on this than on the actual character.
\subsection
{
Speed
}
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@@ -673,6 +672,13 @@ are not properly classified. This is of course very problematic, both for
training the SVM as for checking the performance. This meant we had to check
each character whether its description was correct.
As final note, we would like to state that an, in our eyes, unrealistic amount
of characters has a bad quality, with a lot of dirt, or crooked plates
etcetera. Our own experience is that the average license plate is less hard to
read. The local binary pattern method has proven to work on this set, and as
such has proven that it performs good in worst-case scenarios, but we would
like to see how it performs on a more realistic dataset.
\subsubsection*
{
SVM
}
We also had trouble with the SVM for Python. The standard Python SVM, libsvm,
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