We propose a context-sensitive-chunk based back-propagation 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.
['Kai Chen', 'Zhi-Jie Yan', 'Qiang Huo']
A context-sensitive-chunk BPTT approach to training deep LSTM/BLSTM recurrent neural networks for offline handwriting recognition