restructured presentation

presentations/Bachelor-Short/LaTeX/bachelor-short.tex

@@ -26,13 +26,20 @@
 \section{What is my Bachelor's thesis about?}
 \input{introduction}
 
-\section{What did I do so far?}
-\input{work-done}
+\section{write-math.com}
+\input{write-math}
+
+\section{Preprocessing and Features}
+\input{preprocessing}
+\input{features}
+
+\section{Neural Nets}
+\input{neural-nets}
 
 \section{What will I do next?}
 \input{will-do}
 
-\section{End}
+\section*{End}
 \subsection{End}
 \input{sources}
 \framedgraphic{Thanks for Your Attention!}{../images/xi.png}

presentations/Bachelor-Short/LaTeX/features.tex

@@ -0,0 +1,23 @@
+\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}

presentations/Bachelor-Short/LaTeX/introduction.tex

@@ -2,9 +2,9 @@
 
 \begin{frame}{What is my Bachelor's thesis about?}
     \begin{itemize}
-        \item Recognition of handwritten mathematical formulas
+        \item Recognition of handwritten mathematical symbols
         \item On-line recognition, not OCR!
-        \item Given a series of points $(x(t), y(t), b)$\\
+        \item Given a series of points $(x(t), y(t), b(t))$\\
               I want to get the proper \LaTeX{} code.
     \end{itemize}
 \end{frame}
@@ -16,13 +16,13 @@
         \item It's much harder to find complete formulas.
     \end{itemize}
 
-    I want to
-    \begin{itemize}
-        \item provide a tool that enables beginners to get the best \LaTeX{} code
-              for their formula,
-        \item find out what works best for symbol recognition
-        \item and provide data and a platform to test new ideas for classifiers
-    \end{itemize}
+    % I want to
+    % \begin{itemize}
+    %     \item provide a tool that enables beginners to get the best \LaTeX{} code
+    %           for their formula,
+    %     \item find out what works best for symbol recognition
+    %     \item and provide data and a platform to test new ideas for classifiers
+    % \end{itemize}
 
     As soon as symbol recognition works good in terms of classification time and
     performance, I will continue with formula recognition.

presentations/Bachelor-Short/LaTeX/neural-nets.tex

@@ -0,0 +1,13 @@
+\subsection{Neural Net experiments}
+\begin{frame}{Experiments}
+    \textbf{Preprocessing:} Scaling, shifting and linear interpolation\\
+    \textbf{Features:} Coordinates of 80 points (4 Lines with 20 points each)\\
+    \textbf{Learning:} MLP, 300 epochs, LR of 0.1
+    \begin{itemize}
+        \item[] \textit{toplogy       \tabto{6cm} error in training time}
+        \item 160:500:369             \tabto{6cm} 30.62 \% in \hphantom{0}9min 08s
+        \item 160:500:500:369         \tabto{6cm} 27.73 \% in 11min 49s
+        \item 160:500:500:500:369     \tabto{6cm} 34.79 \% in 14min 09s
+        \item 160:500:500:500:500:369 \tabto{6cm} 33.61 \% in 14min 06s
+    \end{itemize}
+\end{frame}

presentations/Bachelor-Short/LaTeX/preprocessing.tex

@@ -0,0 +1,18 @@
+\subsection{Preprocessing}
+\begin{frame}{Preprocessing}
+    \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}

presentations/Bachelor-Short/LaTeX/will-do.tex

@@ -1,32 +1,22 @@
 \subsection{What will I do next?}
 \begin{frame}{What will I do next?}
     \begin{itemize}
-        \item Get classification performance with cross-validation
-        \item Implement neural net for classification
-        \begin{itemize}
-            \item preprocessing: compute cubic spline for each line
-            \begin{itemize}
-                \item equi-spaced points or
-                \item get equi-timed points
-            \end{itemize}
-            \item 5 - 20 input neurons for each line
-            \item 1076 output neurons (one for each symbol)
-        \end{itemize}
-        \item Get a language model (e.g. by parsing Wikipedia)
-        \item Use ANN with HMM (?)
+        \item Evaluate preprocessing steps
+        \item Try other features
+        \item Try other topologies / trainings (e.g. newbob)
     \end{itemize}
 \end{frame}
 
-\subsection{Far future}
-\begin{frame}{What could be done?}
-    \begin{itemize}
-        \item Make use of audio data in a multimodal approach\\
-              e.g. $R$ and $\mathcal{R}$
-        \item Currently, the Lecture Translation system doesn't recognize math.\\
-              You get \enquote{integral of e raised to the power of x d x} instead
-              of $\int e^x \mathrm{d} x$.
-        \item Spoken math is ambigous: $\sqrt{a+b}$ vs. $\sqrt{a} + b$
-        \item The language model I create could help to find probable formulas
-        \item The platform could be used to get more input data of users
-    \end{itemize}
-\end{frame}
+% \subsection{Far future}
+% \begin{frame}{What could be done?}
+%     \begin{itemize}
+%         \item Make use of audio data in a multimodal approach\\
+%               e.g. $R$ and $\mathcal{R}$
+%         \item Currently, the Lecture Translation system doesn't recognize math.\\
+%               You get \enquote{integral of e raised to the power of x d x} instead
+%               of $\int e^x \mathrm{d} x$.
+%         \item Spoken math is ambigous: $\sqrt{a+b}$ vs. $\sqrt{a} + b$
+%         \item The language model I create could help to find probable formulas
+%         \item The platform could be used to get more input data of users
+%     \end{itemize}
+% \end{frame}

presentations/Bachelor-Short/LaTeX/work-done.tex

@@ -13,25 +13,18 @@
 
 \framedgraphic{Classify}{../images/classify.png}
 \framedgraphic{Workflow}{../images/workflow.png}
-\framedgraphic{User page}{../images/user-page.png}
-\framedgraphic{Information about handwritten-data}{../images/view.png}
-\framedgraphic{Non-mathematical symbols}{../images/yinyang.png}
-\framedgraphic{Training}{../images/train.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}
-\framedgraphic{Symbol page}{../images/symbol.png}
 
-\begin{frame}{Statistics}
+
+\begin{frame}[fragile]{Statistics}
     \begin{itemize}
-        \item 40 users
-        \item 1076 symbols
-        \item 5519 handwritten symbols (e.g. 195 times the letter \enquote{A})
-        \begin{itemize}
-            \item only 264 have 4 lines
-            \item only 36 have 5 lines
-            \item only 16 have 6 lines
-            \item only 19 have 7 lines or more
-            \item none has more than 12 lines
-        \end{itemize}
+        \item 127 users with at least 5 recordings
+        \item 1109 symbols, but only 369 used for experiments
+        \item $\num{235831}$ recordings (e.g. $\num{3486}$ times \verb+\int+)
     \end{itemize}
 \end{frame}
 
@@ -40,12 +33,11 @@
         \item preprocessing: Scale to fit into unit square while keeping the aspect
               ratio
         \item applies dynamic time warping
-        \item compares a new handwritten symbol with every handwritten symbol
+        \item compares a new recording with every recording
               in the database
-        \item[$\Rightarrow$] Classification time is in $\mathcal{O}(\text{handwritten symbols})$,
+        \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 handwritten
-              symbols
+        \item the current server / workflow can only handle about 4000 recordings
         \item[$\Rightarrow$] Another way to classify is necessary
     \end{itemize}
 \end{frame}

presentations/Bachelor-Short/templates/myStyle.sty

@@ -4,9 +4,11 @@
 \InputIfFileExists{../templates/beamerthemekit.sty}{\usepackage{../templates/beamerthemekit}}{\usetheme{Frankfurt}}
 \usefonttheme{professionalfonts}
 
+\usepackage{tabto}
 \usepackage{hyperref}
 \usepackage{lmodern}
 \usepackage{listings}
+\usepackage{siunitx}
 \usepackage{wrapfig}        % see http://en.wikibooks.org/wiki/LaTeX/Floats,_Figures_and_Captions
 \usepackage[utf8]{inputenc} % this is needed for german umlauts
 \usepackage[english]{babel} % this is needed for german umlauts