Combination of context-dependent bidirectional long short-term memory classifiers for robust offline handwriting recognition

The BLSTM classifier 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 classifier. The baseline approach, consisting of context-independent character label, is compared with several context-dependent approaches, modeling the left and right contexts. The results show that context-dependent models improve the recognition rate, and demonstrate the ability of the BLSTM classifier to deal with a large number of character models, without clustering. Furthermore, the context-dependent and context-independent models are complementary, and their combination leads to a robust recognition. We tested our approach with promising results on the RIMES database of Latin script documents.