Today, BLSTM is widely used in recurrent neural network for speech and handwriting recognition. In this present paper a novel type of recurrent neural network is specifically designed for sequence labeling tasks where the data is hard to segment and contains long-range bidirectional interdependencies. For many tasks, BLSTM is useful to have access to future, as well as past, context. We refer keyword spotting method for handwritten documents. It is derived from a neural network-based system for unconstrained handwriting recognition. As such it performs template-free spotting, i.e., it is not necessary for a keyword to appear in the training set. The keyword spotting is done using a modification of the CTC Token Passing algorithm in conjunction with a recurrent neural network and a statistical n-gram language model instead of a bigram language model.
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Keyword spotting, offline handwriting, document analysis, historical documents, neural network, BLSTM.