Updated LBP section in plan.tex.

docs/plan.tex

@@ -99,33 +99,30 @@ at either the left of right side of the image.
 
 \subsection{Local binary patterns}
 
-Once we have separate digits and characters, we intend to use Local Binary
+Once we have separate digits and characters, we intent to use Local Binary
 Patterns to determine what character or digit we are dealing with. Local Binary
-Patters are a way to classify a texture, because it can create a histogram
-which describes the distribution of line directions in the image. Since letters
-on a license plate are mainly build up of straight lines and simple curves, it
-should theoretically be possible to identify these using Local Binary Patterns.
+Patters are a way to classify a texture based on the distribution of edge
+directions in the image. Since letters on a license plate consist mainly of
+straight lines and simple curves, LBP should be suited to identify these.
 
-This will actually be the first thing to implement, since it is not known if it
-will give the desired results. Our first goal is therefore a proof of concept
-that using LBP's is a good way to determine which character we are dealing
-with.
+To our knowledge, LBP has yet not been used in this manner before. Therefore,
+it will be the first thing to implement, to see if it lives up to the
+expectations. When the proof of concept is there, it can be used in the final
+program.
 
-Important to note is that by now, we have transformed this letter to a standard
-size, which eliminates the need to normalize the histograms generated by the
-algorithm.
+Important to note is that due to the normalization of characters before
+applying LBP. Therefore, no further normalization is needed on the histograms.
 
-Once we have a Local Binary Pattern of the character, we use a Support Vector
-Machine to determine what letter we are dealing with. For this, the feature
-vector of the image will be a concatenation of the histograms of the cells in
-the image.
+Given the LBP of a character, a Support Vector Machine can be used to classify
+the character to a character in a learning set. The SVM uses
 
 \subsection{Matching the database}
 
-In order to recognize what character we are dealing with, we use a Support
-Vector Machine. The SVM can be trained with a subsection of the given dataset
-called the ''Learning set''. Once trained, the entire classifier can be saved
-as a Pickle object\footnote{See
+Given the LBP of a character, a Support Vector Machine can be used to classify
+the character to a character in a learning set. The SVM uses the collection of
+histograms of an image as a feature vector.  The SVM can be trained with a
+subsection of the given dataset called the ''Learning set''. Once trained, the
+entire classifier can be saved as a Pickle object\footnote{See
 \url{http://docs.python.org/library/pickle.html}} for later usage.
 
 \end{document}