TY  - CONF
AU  - Volkmar Frinken
AU  - Francisco Zamora
AU  - Salvador España
AU  - Maria Jose Castro
AU  - Andreas Fischer
AU  - Horst Bunke
A2  - ICPR
PY  - 2012//
TI  - Long-Short Term Memory Neural Networks Language Modeling for Handwriting Recognition
BT  - 21st International Conference on Pattern Recognition
SP  - 701
EP  - 704
N2  - 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.
SN  - 1051-4651
SN  - 978-1-4673-2216-4
L1  - http://refbase.cvc.uab.es/files/FZE2012.pdf
N1  - DAG
ID  - Volkmar Frinken2012
ER  -