mirror of
https://github.com/MartinThoma/LaTeX-examples.git
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87 lines
3.4 KiB
TeX
87 lines
3.4 KiB
TeX
\documentclass[9pt,technote,a4paper]{IEEEtran}
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\usepackage{amssymb, amsmath} % needed for math
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\usepackage[a-1b]{pdfx}
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\usepackage{filecontents}
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\begin{filecontents*}{\jobname.xmpdata}
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\Keywords{recognition\sep machine learning\sep neural networks\sep symbols\sep multilayer perceptron}
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\Title{On-line Recognition of Handwritten Mathematical Symbols}
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\Author{Martin Thoma, Kevin Kilgour, Sebastian St{\"u}ker and Alexander Waibel}
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\Org{Institute for Anthropomatics and Robotics}
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\Doi{}
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\end{filecontents*}
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\RequirePackage{ifpdf}
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\ifpdf \PassOptionsToPackage{pdfpagelabels}{hyperref} \fi
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\RequirePackage{hyperref}
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\usepackage{parskip}
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\usepackage[pdftex,final]{graphicx}
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\usepackage{csquotes}
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\usepackage{braket}
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\usepackage{booktabs}
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\usepackage{multirow}
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\usepackage{pgfplots}
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\usepackage{wasysym}
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\usepackage{caption}
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% \captionsetup{belowskip=12pt,aboveskip=4pt}
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\makeatletter
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\newcommand\mynobreakpar{\par\nobreak\@afterheading}
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\makeatother
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\usepackage[noadjust]{cite}
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\usepackage[nameinlink,noabbrev]{cleveref} % has to be after hyperref, ntheorem, amsthm
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\usepackage[binary-units,group-separator={,}]{siunitx}
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\sisetup{per-mode=fraction,binary-units=true}
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\DeclareSIUnit\pixel{px}
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\usepackage{glossaries}
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\loadglsentries[main]{glossary}
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\makeglossaries
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\title{On-line Recognition of Handwritten Mathematical Symbols}
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\author{Martin Thoma, Kevin Kilgour, Sebastian St{\"u}ker and Alexander Waibel}
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\hypersetup{
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pdfauthor = {Martin Thoma\sep Kevin Kilgour\sep Sebastian St{\"u}ker\sep Alexander Waibel},
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pdfkeywords = {recognition\sep machine learning\sep neural networks\sep symbols\sep multilayer perceptron},
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pdfsubject = {Recognition},
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pdftitle = {On-line Recognition of Handwritten Mathematical Symbols},
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}
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\include{variables}
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\crefname{table}{Table}{Tables}
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\crefname{figure}{Figure}{Figures}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% Begin document %
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\begin{document}
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\maketitle
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\begin{abstract}
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The automatic recognition of single handwritten symbols has three main
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applications: Supporting users who know how a symbol looks like, but not what
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its name is, providing the necessary commands for professional publishing, or
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as a building block for formula recognition.
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This paper presents a system which uses the pen trajectory to classify
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handwritten symbols. Five preprocessing steps, one data multiplication
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algorithm, five features and five variants for multilayer Perceptron training
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were evaluated using $\num{166898}$ recordings. Those recordings were made
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publicly available. The evaluation results of these 21~experiments were used to
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create an optimized recognizer which has a top-1 error of less than
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$\SI{17.5}{\percent}$ and a top-3 error of $\SI{4.0}{\percent}$. This is a
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relative improvement of $\SI{18.5}{\percent}$ for the top-1 error and
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$\SI{29.7}{\percent}$ for the top-3 error compared to the baseline system. This
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improvement was achieved by \acrlong{SLP} and adding new features. The
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improved classifier can be used via \href{http://write-math.com/}{write-math.com}.
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\end{abstract}
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\input{ch1-introduction}
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\input{ch2-general-system-design}
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\input{ch3-data-and-implementation}
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\input{ch4-algorithms}
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\input{ch5-optimization-of-system-design}
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\input{ch6-summary}
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\input{ch7-mfrdb-eval}
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\bibliographystyle{IEEEtranSA}
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\bibliography{write-math-ba-paper}
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\end{document}
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