  State-of-the-art methods for handwriting recognition are based on Long Short
Term Memory (LSTM) recurrent neural networks (RNN), which now provides very
impressive character recognition performance. The character recognition is
generally coupled with a lexicon driven decoding process which integrates
dictionaries. Unfortunately these dictionaries are limited to hundred of
thousands words for the best systems, which prevent from having a good language
coverage, and therefore limit the global recognition performance. In this
article, we propose an alternative to the lexicon driven decoding process based
on a lexicon verification process, coupled with an original cascade
architecture. The cascade is made of a large number of complementary networks
extracted from a single training (called cohort), making the learning process
very light. The proposed method achieves new state-of-the art word recognition
performance on the Rimes and IAM databases. Dealing with gigantic lexicon of 3
millions words, the methods also demonstrates interesting performance with a
fast decision stage.

Computer Vision and Pattern Recognition