Context-dependent BLSTM models. Application to offline handwriting recognition

The BLSTM model has been recently introduced for sequence labeling tasks and provides state-of-the-art performance for handwriting recognition. Its recurrent connections integrate the context at the feature level over a long range. Nevertheless, this context is not explicitly modeled at the label level. Explicit context-modeling strategies have been applied to HMMs with improvement of the recognition rate. In this paper, we study the effect of context modeling on the performance of the BLSTM model. The baseline BLSTM, with context-independent character label, is compared with two context-dependent BLSTM, one modeling the left context and the other the right context. The results show that context-dependent models provide an improvement of the recognition rate, and demonstrate the ability of the BLSTM model to deal with a large number of models, without clustering. We tested our models on the RIMES database of Latin script documents.