# Ubicomp Digital 2020 -- Handwriting classification using a convolutional recurrent network

arXiv: https://arxiv.org/abs/2008.01078 · HTML: https://mlsanity.com/paper/2008.01078/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2020.

## Built on by (most cited first, 2 of 2)

- [Detecting Handwritten Mathematical Terms with Sensor Based Data](https://mlsanity.com/paper/2109.05594/) (2021)
- [Telling Human and Machine Handwriting Apart](https://mlsanity.com/paper/2601.11700/) (2026)

## Abstract (arXiv)

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.