The Ubicomp Digital 2020 -- Time Series Classification Challenge from STABILO is a challenge about multi-variate time series classification. The data collected from 100 volunteer writers, and contains 15 features measured with multiple sensors on a pen. In this paper,we use a neural network to classify the data into 52 classes, that is lower and upper cases of Arabic letters. The proposed architecture of the neural network a is CNN-LSTM network. It combines convolutional neural network (CNN) for short term context with along short term memory layer (LSTM) for also long term dependencies. We reached an accuracy of 68% on our writer exclusive test set and64.6% on the blind challenge test set resulting in the second place.
@article{arxiv.2008.01078,
title = {Ubicomp Digital 2020 -- Handwriting classification using a convolutional recurrent network},
author = {Wei-Cheng Lai and Hendrik Schröter},
journal= {arXiv preprint arXiv:2008.01078},
year = {2020}
}