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- %!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}.
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