  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.
