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Commit a7c85d52 authored by Jayke Meijer's avatar Jayke Meijer
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Changed results of grid search.

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......@@ -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/results\_\{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 $0.932^6 = 0.655$, so $65.5\%$. That is not particularly
get a score of $0.936^6 = 0.672$, so $67.2\%$. 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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