LaTeX-examples/documents/papers/write-math-paper/write-math-ba-paper.tex

\documentclass[9pt,technote,a4paper]{IEEEtran}
\usepackage{amssymb, amsmath} % needed for math

\usepackage[a-1b]{pdfx}
\usepackage{filecontents}
\begin{filecontents*}{\jobname.xmpdata}
    \Keywords{recognition\sep machine learning\sep neural networks\sep symbols\sep multilayer perceptron}
    \Title{On-line Recognition of Handwritten Mathematical Symbols}
    \Author{Martin Thoma, Kevin Kilgour, Sebastian St{\"u}ker and Alexander Waibel}
    \Org{Institute for Anthropomatics and Robotics}
    \Doi{}
\end{filecontents*}

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\title{On-line Recognition of Handwritten Mathematical Symbols}
\author{Martin Thoma, Kevin Kilgour, Sebastian St{\"u}ker and Alexander Waibel}

\hypersetup{
  pdfauthor   = {Martin Thoma\sep Kevin Kilgour\sep Sebastian St{\"u}ker\sep Alexander Waibel},
  pdfkeywords = {recognition\sep machine learning\sep neural networks\sep symbols\sep multilayer perceptron},
  pdfsubject  = {Recognition},
  pdftitle    = {On-line Recognition of Handwritten Mathematical Symbols},
}
\include{variables}
\crefname{table}{Table}{Tables}
\crefname{figure}{Figure}{Figures}

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\begin{document}
\maketitle
\begin{abstract}
The automatic recognition of single handwritten symbols has three main
applications: Supporting users who know how a symbol looks like, but not what
its name is, providing the necessary commands for professional publishing, or
as a building block for formula recognition.

This paper presents a system which uses the pen trajectory to classify
handwritten symbols. Five preprocessing steps, one data multiplication
algorithm, five features and five variants for multilayer Perceptron training
were evaluated using $\num{166898}$ recordings. Those recordings were made
publicly available. The evaluation results of these 21~experiments were used to
create an optimized recognizer which has a top-1 error of less than
$\SI{17.5}{\percent}$ and a top-3 error of $\SI{4.0}{\percent}$. This is a
relative improvement of $\SI{18.5}{\percent}$ for the top-1 error and
$\SI{29.7}{\percent}$ for the top-3 error compared to the baseline system. This
improvement was achieved by \acrlong{SLP} and adding new features. The
improved classifier can be used via \href{http://write-math.com/}{write-math.com}.
\end{abstract}

\input{ch1-introduction}
\input{ch2-general-system-design}
\input{ch3-data-and-implementation}
\input{ch4-algorithms}
\input{ch5-optimization-of-system-design}
\input{ch6-summary}
\input{ch7-mfrdb-eval}


\bibliographystyle{IEEEtranSA}
\bibliography{write-math-ba-paper}
\end{document}