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This abstract explores an RNN-based approach to online handwrittenrecognition problem. Our method uses data from an accelerometer and a gyroscopemounted on a handheld pen-like device to train and run a character pre-dictionmodel. We have built a dataset of timestamped gyroscope and accelerometer datagathered during the manual process of handwriting Latin characters, labeledwith the character being written; in total, the dataset con-sists of 1500gyroscope and accelerometer data sequenc-es for 8 characters of the Latinalphabet from 6 different people, and 20 characters, each 1500 samples fromGeorgian alphabet from 5 different people. with each sequence containing thegyroscope and accelerometer data captured during the writing of a particularcharacter sampled once every 10ms. We train an RNN-based neural networkarchitecture on this dataset to predict the character being written. The modelis optimized with categorical cross-entropy loss and RMSprop optimizer andachieves high accuracy on test data.

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