# Motion-Based Handwriting Recognition

arXiv: https://arxiv.org/abs/2101.06022 · HTML: https://mlsanity.com/paper/2101.06022/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 15 Jan 2021.

## Builds on (most cited first, 2 of 2)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Kinect Sensor Based Gesture Recognition for Surveillance Application](https://mlsanity.com/paper/1812.09595/) (2018)

## Built on by (most cited first, 1 of 1)

- [Auxiliary Cross-Modal Representation Learning with Triplet Loss Functions for Online Handwriting Recognition](https://mlsanity.com/paper/2202.07901/) (2022)

## Abstract (arXiv)

We attempt to overcome the restriction of requiring a writing surface for handwriting recognition. In this study, we design a prototype of a stylus equipped with motion sensor, and utilizes gyroscopic and acceleration sensor reading to perform written letter classification using various deep learning techniques such as CNN and RNNs. We also explore various data augmentation techniques and their effects, reaching up to 86% accuracy.