  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.
