Worked on report.

docs/report.tex

@@ -652,46 +652,47 @@ were and whether we were able to find a proper solution for them.
 
 \subsubsection*{Dataset}
 
-We did experience a number of problems with the provided dataset. A number of
-these are problems to be expected in the real world, but which make development
-harder. Others are more elemental problems.
+We have encountered a number of problems with the provided dataset. A number of
+these are to be expected in the real world, but they do make development
+harder. Others are more elementary problems.
 
-The first problem was that the dataset contains a lot of license plates which
+The first problem is that the dataset contains a lot of license plates which
 are problematic to read, due to excessive amounts of dirt on them. Of course,
 this is something you would encounter in the real situation, but it made it
 hard for us to see whether there was a coding error or just a bad example.
 
-Another problem was that there were license plates of several countries in
+Another problem is that there were license plates of several countries in
 the dataset. Each of these countries has it own font, which also makes it
 hard to identify these plates, unless there are a lot of these plates in the
 learning set.
 
 A problem that is more elemental is that some of the characters in the dataset
-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.
+are not properly classified. This is obviously very problematic, because it
+means that we had to manually verify the value of each character.
 
-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
+As final note, we would like to state that a seemingly unrealistic amount
+of characters has a poor quality, with a lot of dirt, or crooked plates
+etc.. 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.
+like to see how it performs on a dataset with a larger amount of readable,
+higher-resolution characters.
 
-\subsubsection*{SVM}
+\subsubsection*{\texttt{libsvm}}
 
-We also had trouble with the SVM for Python. The standard Python SVM, libsvm,
-had a poor documentation. There was no explanation what so ever on which
-parameter had to be what. This made it a lot harder for us to see what went
-wrong in the program.
+We also had trouble with the SVM for Python. The standard Python SVM,
+\texttt{libsvm}, had a poor documentation. There was no documentation
+whatsoever for a number of functions. This did not improve efficiency during
+the process of development.
 
 \subsection{Workload distribution}
 
-The first two weeks were team based. Basically the LBP algorithm could be
-implemented in the first hour, while some talked and someone did the typing.
-Some additional 'basics' where created in similar fashion. This ensured that
-every team member was up-to-date and could start figuring out which part of the
-implementation was most suited to be done by one individually or in a pair.
+The first two weeks were very team based. Basically, the LBP algorithm day
+implemented in the first day, as result of a collective effort. Some
+additional `basic' functions and classes were created in similar fashion. This
+ensured that every team member was up-to-date and could start figuring out
+which part of the implementation was most suited to be done by one individually
+or in a pair.
 
 \subsubsection*{Who did what}