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