Made corrections and changes in dataset.tex.

report/dataset.tex

@@ -1,13 +1,17 @@
 
-We tried to find a dataset with handwritten text at the beginning of the project, but it turns out there are not that many available. 
-% Not sure what Image is supposed to be referenced here.
-The datasets that do exist, like following image example(Figure 2), they would have needed a lot of preprocessing before we could use them in our project.
-We would have had to implement baseline slant normalization, skew correction, skeleton and so on.
+An attempt was made to find a dataset with handwritten text, but we failed to find one that suited our requirements.
+The datasets that were found would require a lot of preprocessing. 
+Figure~\ref{} shows a sample from that kind of dataset.
+To get good result from that kind of dataset it would be necessary to implement baseline slant normalization, skew correction, skeleton and so on.
 
 Therefore, instead of spending a lot of time preprocessing the datasets, we implemented a Graphic User Interface to create our own dataset.
-The biggest advantages of this solution is that our solution records one pixel wide letters and the characters are already separated. 
-The most important part of the work, image processing, was thus reduced significantly.
+The largest advantages of this solution is that our solution records one pixel wide lines and the characters are already separated. 
+The large part of the work, image processing, was thus reduced significantly.
+Our dataset contains 100 examples for every capital letter in the Latin alphabet
+\footnote{The dataset is available together with the source code of the system. See appendix~\ref{app:source_code}.}.
+An example image from our character image dataset can be found in Figure~\ref{fig:image_feature_extraction}.
 
-Furthermore, if the vocabulary is relatively large, we found that it became easier for us to test the HMM.
-This is because our word training data is made up of randomly chosen samples.
+To get a dataset for training the word classifier a generator was created\footnote{Please see HandReco\/src\/api\/word\_examples\_generator.py in the source code for documentation of the word example generator. See appendix~\ref{app:source_code}.}.
+The generator creates random errors in the words given as input.
+To generate the dataset is obviously not optimal for practical applications, but it is good enough to test the implementation.