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2007

Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks

8 years 5 months ago
Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks
In online handwriting recognition the trajectory of the pen is recorded during writing. Although the trajectory provides a compact and complete representation of the written output, it is hard to transcribe directly, because each letter is spread over many pen locations. Most recognition systems therefore employ sophisticated preprocessing techniques to put the inputs into a more localised form. However these techniques require considerable human effort, and are specific to particular languages and alphabets. This paper describes a system capable of directly transcribing raw online handwriting data. The system consists of an advanced recurrent neural network with an output layer designed for sequence labelling, combined with a probabilistic language model. In experiments on an unconstrained online database, we record excellent results using either raw or preprocessed data, well outperforming a state-of-the-art HMM based system in both cases.
Alex Graves, Santiago Fernández, Marcus Liw
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2007
Where NIPS
Authors Alex Graves, Santiago Fernández, Marcus Liwicki, Horst Bunke, Jürgen Schmidhuber
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