  Most online handwriting recognition systems require the use of specific
writing surfaces to extract positional data. In this paper we present a online
handwriting recognition system for word recognition which is based on inertial
measurement units (IMUs) for digitizing text written on paper. This is obtained
by means of a sensor-equipped pen that provides acceleration, angular velocity,
and magnetic forces streamed via Bluetooth. Our model combines convolutional
and bidirectional LSTM networks, and is trained with the Connectionist Temporal
Classification loss that allows the interpretation of raw sensor data into
words without the need of sequence segmentation. We use a dataset of words
collected using multiple sensor-enhanced pens and evaluate our model on
distinct test sets of seen and unseen words achieving a character error rate of
17.97% and 17.08%, respectively, without the use of a dictionary or language
model
