title: Cortical-Inspired Open-Bigram Representation for Handwritten Word Recognition
abstract: Recent research in the cognitive process of reading hypothesized that we do
  not read words by sequentially recognizing letters, but rather by identifing
  open-bigrams, i.e. couple of letters that are not necessarily next
  to each other. 
  In this paper, we evaluate an handwritten word recognition method based on original
  open-bigrams representation. We trained Long Short-Term Memory Recurrent Neural Networks
  (LSTM-RNNs) to predict open-bigrams rather than characters, and we show that
  such models are able to learn the long-range, complicated and intertwined dependencies
  in the input signal, necessary to the prediction. 
  For decoding, we decomposed each word of a large vocabulary into the set of
  constituent bigrams, and apply a simple cosine similarity measure between this 
  representation and the bagged RNN prediction to retrieve the vocabulary word. 
  We compare this method to standard word recognition techniques based on 
  sequential character recognition.
  Experiments are carried out on two public databases of handwritten words
  (Rimes and IAM), an the results with our bigram decoder are comparable 
  to more conventional decoding methods based on sequences of letters.
authors: Théodore Bluche Christopher Kermorvant Claude Touzet Hervé Glotin
hIndex: Hervé Glotin 32