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Martin Thoma 2014-11-02 13:27:06 +01:00
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commit f3bc84df5d
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\subsection{Preprocessing}
\begin{frame}{Preprocessing}
\subsection{Preprocessing Algorithms}
\begin{frame}{Preprocessing Algorithms}
\begin{columns}[T] % contents are top vertically aligned
\begin{column}[T]{5cm} % each column can also be its own environment
\begin{itemize}
@ -14,7 +14,7 @@
\item<4-> Smoothing (e.g. moving average)
\item<5-> Dot reduction
\item<6-> Filtering (by distance, speed or angle)
\item<7-> Stroke connection
\item<8-> Stroke connection
\end{itemize}
\end{itemize}
\end{column}
@ -22,7 +22,10 @@
\only<2>{\includegraphics[width=6cm, keepaspectratio]{scale-and-shift.png}}
\only<3>{\includegraphics[width=6cm, keepaspectratio]{resampling.png}}
\only<4>{\includegraphics[width=6cm, keepaspectratio]{smooth-1-1-1.png}}
\only<7>{\includegraphics[width=6cm, keepaspectratio]{interrupted-stroke.png}}
\only<5>{\includegraphics[width=6cm, keepaspectratio]{dot-reduction.png}}
\only<6>{\includegraphics[width=6cm, keepaspectratio]{wildpoint-1.png}}
\only<7>{\includegraphics[width=6cm, keepaspectratio]{wildpoint-2.png}}
\only<8>{\includegraphics[width=6cm, keepaspectratio]{interrupted-stroke.png}}
\end{column}
\end{columns}

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\subsection{Write Math}
\begin{frame}{write-math.com}
\begin{itemize}
\item a website where users can add labeled training data and unlabeled
data which they want to classify. I call this data \enquote{recording}
\begin{figure}[ht]
\centering
\subfloat{
\includegraphics[height=0.1\textwidth]{../images/279952.pdf}
}%
\qquad
\subfloat{
\includegraphics[height=0.1\textwidth]{../images/281507.pdf}
}%
\qquad
\subfloat{
\includegraphics[height=0.1\textwidth]{../images/287612.pdf}
}%
\qquad
\subfloat{
\includegraphics[height=0.1\textwidth]{../images/292175.pdf}
}%
\caption*{4 recordings}
\end{figure}
\item works with desktop computers and touch devices
\item symbol recognition can be done by multiple classifiers
\item users can contribute formulas as recordings and as \LaTeX{} answers
for recordings
\item users can vote for \LaTeX{} answers:
\Large $\leq$, $\leqq$, $\leqslant$, \dots \normalsize
\item user who entered the recording can accept one answer
\end{itemize}
\end{frame}
% \framedgraphic{Classify}{../images/classify.png}
% \framedgraphic{Workflow}{../images/workflow.png}
% \framedgraphic{User page}{../images/user-page.png}
% \framedgraphic{Information about recordings}{../images/view.png}
% \framedgraphic{Symbol page}{../images/symbol.png}
% \framedgraphic{Training}{../images/train.png}
% \framedgraphic{Ranking}{../images/ranking.png}
\begin{frame}[fragile]{Statistics}
\begin{itemize}
\item 127 users with at least 5 recordings
\item $\num{1111}$ symbols, but only $\num{369}$ used for experiments
\item $\num{235831}$ recordings (e.g. $\num{3489}$ times \verb+\int+, but only 50 times \verb+X+)
\end{itemize}
\end{frame}
% \begin{frame}{First classification worker}
% \begin{itemize}
% \item preprocessing: Scale to fit into unit square while keeping the aspect
% ratio
% \item applies greedy time warping
% \item compares a new recording with every recording
% in the database
% \item[$\Rightarrow$] Classification time is in $\mathcal{O}(\text{recordings})$,
% but we rather would like $\mathcal{O}(\text{symbols})$
% \item the current server / workflow can only handle about 4000 recordings
% \item[$\Rightarrow$] Another way to classify is necessary
% \end{itemize}
% \end{frame}
\begin{frame}[fragile]{Handwriting Recognition Toolkit (hwrt)}
\begin{itemize}
\item Many preprocessing algorithms / features implemented
\item Plug-in system for preprocessing algorithms / features
\item Needs neural network toolkit
\item Hosted at \url{https://github.com/MartinThoma/hwrt}
\item Installable via \verb+pip+ (Python package installer):\\
\verb+$ pip install hwrt+
\end{itemize}
\end{frame}
\begin{frame}[fragile]{hwrt preprocessing configuration file}
\begin{verbatim}
data-source: raw-datasets/2014-08-26-20-14-data-raw.pickle
queue:
- RemoveDuplicateTime: null
- ScaleAndShift:
- max_width: 1.0
- max_height: 1.0
- center: true
- SpaceEvenlyPerStroke:
- kind: linear
- number: 20
\end{verbatim}
\end{frame}
\subsection{Preprocessing algorithms}
\begin{frame}{Preprocessing algorithms}
\begin{itemize}
\item Normalizing
\begin{itemize}
\item Scaling
\item Shifting
\item Resampling
\end{itemize}
\item Noise reduction
\begin{itemize}
\item Smoothing (e.g. moving average)
\item Dot reduction
\item Filtering (by distance, speed or angle)
\item Stroke connection
\end{itemize}
\end{itemize}
\end{frame}
\begin{frame}[fragile]{hwrt feature configuration file}
\begin{verbatim}
data-source: preprocessed/baseline
data-multiplication:
- Multiply:
- nr: 1
features:
- ConstantPointCoordinates:
- strokes: 4
- points_per_stroke: 20
- fill_empty_with: 0
- pen_down: false
\end{verbatim}
\end{frame}
\subsection{Features}
\begin{frame}{Features}
\begin{itemize}
\item Local
\begin{itemize}
\item Coordinates
\item Speed
\item Binary pen pressure
\item Direction
\item Curvature
\item Bitmap-environment
\item Hat-Feature
\end{itemize}
\item Global
\begin{itemize}
\item \# of points
\item \# of strokes
\item Center point
\item Bitmap
\item Bounding box (width, height, time)
\end{itemize}
\end{itemize}
\end{frame}