Ubicomp Digital 2020 -- Handwriting classification using a convolutional recurrent network
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 class...
Saved in:
Main Authors | , |
---|---|
Format | Journal Article |
Language | English |
Published |
03.08.2020
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | 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. |
---|---|
DOI: | 10.48550/arxiv.2008.01078 |