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Recurrent neural networks (RNNs) with Long Short-Term memory cells currentlyhold the best known results in unconstrained handwriting recognition. We showthat their performance can be greatly improved using dropout - a recentlyproposed regularization method for deep architectures. While previous worksshowed that dropout gave superior performance in the context of convolutionalnetworks, it had never been applied to RNNs. In our approach, dropout iscarefully used in the network so that it does not affect the recurrentconnections, hence the power of RNNs in modeling sequence is preserved.Extensive experiments on a broad range of handwritten databases confirm theeffectiveness of dropout on deep architectures even when the network mainlyconsists of recurrent and shared connections.

Dropout improves Recurrent Neural Networks for Handwriting Recognition