We propose a context-sensitive-chunk based backpropagation through time (BPTT) approach to training deep
(bidirectional) long short-term memory ((B)LSTM) recurrent neural networks (RNN) that splits each training sequence into chunks with appended contextual observations for character modeling of offline handwriting recognition. Using short context sensitive chunks in both training and recognition brings following benefits: (1) the learned (B)LSTM will model mainly local character image dependency and the effect of long-range language model information reflected in training data is reduced; (2) mini-batch based training on GPU can be made more efficient; (3) low-latency BLSTM-based handwriting recognition is made possible by incurring only a delay of a short chunk rather than a whole sentence. Our approach is evaluated on IAM offline handwriting recognition benchmark task and performs better than the previous state-of-the-art BPTT-based approaches.