FORWARD-BACKWARD RETRAINING OF RECURRENT NEURAL NETWORKS
Andrew Senior and Tony Robinson
This paper describes the training of a recurrent neural network as the letter posterior probability estimator for a hidden Markov model, off-line handwriting recognition system. The network estimates posterior distributions for each of a series of frames representing sections of a handwritten word. The supervised training algorithm, backpropagation through time, requires target outputs to be provided for each frame. Three methods for deriving these targets are presented. A novel method based upon the forward-backward algorithm is found to result in the recognizer with the lowest error rate.
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