Cascading BLSTM Networks For Handwritten Word Recognition

Handwritten word recognition is a tough task, mixing image and natural language processing. Recently new recurrent neural networks with LSTM cells allowed significant improvements in this field. These networks are generally coupled with lexical and linguistic knowledge in order to correct character misrecognitions, namely using a lexicon driven decoding. Yet the high performances of LSTM networks let us think that there is a room to use them without lexical decoding. In this article we propose a lexicon-free decoding, combined with a lexicon verification method. This lexicon control method presents some interesting properties and enables us to efficiently combine LSTM networks in a cascade framework. This cascade process is not driven but simply controlled by the lexicon, allowing it to speed up the decoding while being nearly insensitive to the lexicon size. Our approach presents promising results with low error rate by conceding rejects. Those rejects can finally be processed by a standard lexical decoding, enabling us to reach state of the art performance, while being much faster than existing methods for decoding.