%!TEX root = write-math-ba-paper.tex \section{General System Design} The following steps are used for symbol classification:\nobreak \begin{enumerate} \item \textbf{Preprocessing}: Recorded data is never perfect. Devices have errors and people make mistakes while using the devices. To tackle these problems there are preprocessing algorithms to clean the data. The preprocessing algorithms can also remove unnecessary variations of the data that do not help in the classification process, but hide what is important. Having slightly different sizes of the same symbol is an example of such a variation. Four preprocessing algorithms that clean or normalize recordings are explained in \cref{sec:preprocessing}. \item \textbf{Data multiplication}: Learning systems need lots of data to learn internal parameters. If there is not enough data available, domain knowledge can be considered to create new artificial data from the original data. In the domain of on-line handwriting recognition, data can be multiplied by adding rotated variants. \item \textbf{Feature extraction}: A feature is high-level information derived from the raw data after preprocessing. Some systems like Detexify take the result of the preprocessing step, but many compute new features. Those features can be designed by a human engineer or learned. Non-raw data features have the advantage that less training data is needed since the developer uses knowledge about handwriting to compute highly discriminative features. Various features are explained in \cref{sec:features}. \end{enumerate} After these steps, it is a classification task for which the classifier has to learn internal parameters before it can classify new recordings.We classified recordings by computing constant-sized feature vectors and using \glspl{MLP}. There are many ways to adjust \glspl{MLP} (number of neurons and layers, activation functions) and their training (learning rate, momentum, error function). Some of them are described in~\cref{sec:mlp-training} and the evaluation results are presented in \cref{ch:Optimization-of-System-Design}.