Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks

Graves, Alex, Liwicki, Marcus, Bunke, Horst, Schmidhuber, Jürgen, Fernández, Santiago

Neural Information Processing Systems 

On-line handwriting recognition is unusual among sequence labelling tasks in that the underlying generator of the observed data, i.e. the movement of the pen, is recorded directly. However, the raw data can be difficult to interpret because each letter is spread over many pen locations. As a consequence, sophisticated pre-processing is required to obtain inputs suitable for conventional sequence labelling algorithms, such as HMMs. In this paper we describe a system capable of directly transcribing raw on-line handwriting data. The system consists of a recurrent neural network trained for sequence labelling, combined with a probabilistic language model.