Long-short term memory neural networks language modeling for handwriting recognition

Unconstrained handwritten text recognition systems maximize the combination of two separate probability scores. The first one is the observation probability that indicates how well the returned word sequence matches the input image. The second score is the probability that reflects how likely a word sequence is according to a language model. Current state-of-the-art recognition systems use statistical language models in form of bigram word probabilities. This paper proposes to model the target language by means of a recurrent neural network with long-short term memory cells. Because the network is recurrent, the considered context is not limited to a fixed size especially as the memory cells are designed to deal with long-term dependencies. In a set of experiments conducted on the IAM off-line database we show the superiority of the proposed language model over statistical n-gram models.

[1]  Lukás Burget,et al.  Recurrent neural network based language model , 2010, INTERSPEECH.

[2]  Yoshua Bengio,et al.  A Neural Probabilistic Language Model , 2003, J. Mach. Learn. Res..

[3]  Holger Schwenk,et al.  Continuous space language models , 2007, Comput. Speech Lang..

[4]  Horst Bunke,et al.  The IAM-database: an English sentence database for offline handwriting recognition , 2002, International Journal on Document Analysis and Recognition.

[5]  Jürgen Schmidhuber,et al.  Learning Precise Timing with LSTM Recurrent Networks , 2003, J. Mach. Learn. Res..

[6]  Robert Sabourin,et al.  Large vocabulary off-line handwriting recognition: A survey , 2003, Pattern Analysis & Applications.

[7]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[8]  Holger Schwenk,et al.  N-gram-based machine translation enhanced with neural networks , 2010, IWSLT.

[9]  Horst Bunke,et al.  Using a Statistical Language Model to Improve the Performance of an HMM-Based Cursive Handwriting Recognition System , 2001, Int. J. Pattern Recognit. Artif. Intell..

[10]  Geoffrey Leech,et al.  The tagged LOB Corpus : user's manual , 1986 .