# RNN-based Online Handwritten Character Recognition Using Accelerometer and Gyroscope Data

## TL;DR

This paper presents an RNN-based system that recognizes handwritten Latin and Georgian characters in real-time using accelerometer and gyroscope data from a handheld device, demonstrating high accuracy with a new dataset.

## Contribution

It introduces a novel dataset of accelerometer and gyroscope data for handwritten characters and trains an RNN model for accurate online recognition.

## Key findings

- High accuracy achieved on test data
- Effective use of accelerometer and gyroscope data for recognition
- Dataset includes diverse characters and multiple writers

## Abstract

This abstract explores an RNN-based approach to online handwritten recognition problem. Our method uses data from an accelerometer and a gyroscope mounted on a handheld pen-like device to train and run a character pre-diction model. We have built a dataset of timestamped gyroscope and accelerometer data gathered during the manual process of handwriting Latin characters, labeled with the character being written; in total, the dataset con-sists of 1500 gyroscope and accelerometer data sequenc-es for 8 characters of the Latin alphabet from 6 different people, and 20 characters, each 1500 samples from Georgian alphabet from 5 different people. with each sequence containing the gyroscope and accelerometer data captured during the writing of a particular character sampled once every 10ms. We train an RNN-based neural network architecture on this dataset to predict the character being written. The model is optimized with categorical cross-entropy loss and RMSprop optimizer and achieves high accuracy on test data.

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Source: https://tomesphere.com/paper/1907.12935