|
|
@@ -68,9 +68,29 @@ correct modules to handle images, Python can be decent in speed.
|
|
|
Now we know what our program has to be capable of, we can start with the
|
|
|
defining what problems we have and how we want to solve these.
|
|
|
|
|
|
-\subsection{Extracting a letter and resizing it}
|
|
|
-
|
|
|
-Rewrite this section once we have implemented this properly.
|
|
|
+\subsection{Extracting a character and resizing it}
|
|
|
+
|
|
|
+We need to extract a character from a photo made of a car. We do not have to
|
|
|
+find where in this image the characters are, since this is provided in an XML
|
|
|
+file with our dataset.
|
|
|
+
|
|
|
+Once we have extracted the points from this XML file, we need to get this
|
|
|
+character from the image. For the nature of the Local Binary Pattern algorithm,
|
|
|
+we want a margin around the character. However, the points stored in the XML
|
|
|
+file are chosen in such a fashion, that the character would be cut out exactly.
|
|
|
+Therefore, we choose to take points that are slightly outside of the given
|
|
|
+points.
|
|
|
+
|
|
|
+When we have the points we want, we use a perspective transformation to get
|
|
|
+an exact image of the character.
|
|
|
+
|
|
|
+The final step is to resize this image in such a fashion, that the stroke
|
|
|
+of the character is more or less equal in each image. We do this by setting
|
|
|
+the height to a standard size, since each character has the same height on a
|
|
|
+license plate. We retain the height-width ratio, so we do not end up with
|
|
|
+characters that are different than other examples of the same character,
|
|
|
+because the image got stretched, which would of course be a bad thing for
|
|
|
+the classification.
|
|
|
|
|
|
\subsection{Transformation}
|
|
|
|