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This paper presents an evaluation of deep neural networks for recognition ofdigits entered by users on a smartphone touchscreen. A new large dataset ofArabic numerals was collected for training and evaluation of the network. Thedataset consists of spatial and temporal touch data recorded for 80 digitsentered by 260 users. Two neural network models were investigated. The firstmodel was a 2D convolutional neural (ConvNet) network applied to bitmaps of theglpyhs created by interpolation of the sensed screen touches and its topologyis similar to that of previously published models for offline handwritingrecognition from scanned images. The second model used a 1D ConvNetarchitecture but was applied to the sequence of polar vectors connecting thetouch 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 thenumber of parameters from 1,663,370 to 287,690. The dataset has been madeavailable to the community as an open source resource.

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