  In this work we propose a solution to the UbiComp 2021 Challenge by Stabilo
in which handwritten mathematical terms are supposed to be automatically
classified based on time series sensor data captured on the DigiPen. The input
data set contains data of different writers, with label strings constructed
from a total of 15 different possible characters. The label should first be
split into separate characters to classify them one by one. This issue is
solved by applying a data-dependant and rule-based information extraction
algorithm to the labeled data. Using the resulting data, two classifiers are
constructed. The first is a binary classifier that is able to predict, for
unknown data, if a sample is part of a writing activity, and consists of a Deep
Neural Network feature extractor in concatenation with a Random Forest that is
trained to classify the extracted features at an F1 score of >90%. The second
classifier is a Deep Neural Network that combines convolution layers with
recurrent layers to predict windows with a single label, out of the 15 possible
classes, at an F1 score of >60%. A simulation of the challenge evaluation
procedure reports a Levensthein Distance of 8 and shows that the chosen
approach still lacks in overall accuracy and real-time applicability.
