Open Source Dataset and Deep Learning Models for Online Digit Gesture Recognition on Touchscreens

TL;DR

This paper evaluates deep neural networks for recognizing digit gestures on smartphones, introducing a new large dataset of Arabic numerals and comparing two models with high accuracy and reduced complexity.

Contribution

It presents a new large open-source dataset of Arabic digit gestures and compares two neural network architectures for recognition on touchscreen data.

Findings

2D ConvNet achieved 98.50% accuracy

1D ConvNet achieved 95.86% accuracy

The simpler 1D model significantly reduced parameters

Abstract

This paper presents an evaluation of deep neural networks for recognition of digits entered by users on a smartphone touchscreen. A new large dataset of Arabic numerals was collected for training and evaluation of the network. The dataset consists of spatial and temporal touch data recorded for 80 digits entered by 260 users. Two neural network models were investigated. The first model was a 2D convolutional neural (ConvNet) network applied to bitmaps of the glpyhs created by interpolation of the sensed screen touches and its topology is similar to that of previously published models for offline handwriting recognition from scanned images. The second model used a 1D ConvNet architecture but was applied to the sequence of polar vectors connecting the touch points. The models were found to provide accuracies of 98.50% and 95.86%, respectively. The second model was much simpler, providing a reduction in the number of parameters from 1,663,370 to 287,690. The dataset has been made available to the community as an open source resource.