Lai WC, Schröter H (2020)
Publication Status: Submitted
Publication Type: Other publication type
Future Publication Type: Conference contribution
Publication year: 2020
Open Access Link: https://arxiv.org/abs/2008.01078
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.
APA:
Lai, W.-C., & Schröter, H. (2020). Ubicomp Digital 2020 - Handwriting classification using a convolutional recurrent network.
MLA:
Lai, Wei-Cheng, and Hendrik Schröter. Ubicomp Digital 2020 - Handwriting classification using a convolutional recurrent network. 2020.
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