Cursive script online character recognition with a recurrent neural network model

A study was conducted to assess the performance of a discrete-time recurrent neural network in cursive script character recognition. The pen coordinates were sampled at discrete times and sequentially entered on two separate channels to a bank of neural-network-based recognizers, each trained to recognize one specific character. The recognizers´ outputs were collected and reconverted into a string of characters, with associated probabilities. This method was tried on a restricted alphabet of six letters. The results of the study are presented, and its extension to more complex situations is discussed