|Subject Domain (in DDC)||Computer science, information & general works ♦ Data processing & computer science|
|Subject Keyword||Posterior Distribution ♦ Forward-backward Retraining ♦ Supervised Training Algorithm ♦ Target Output ♦ Error Rate ♦ Cheque Processing ♦ Machine Transcription ♦ Novel Method ♦ Handwritten Word ♦ Previous Publication ♦ Recurrent Neural Network ♦ Handwritten Document ♦ Historical Document Reading ♦ Personal Correspondence Reading ♦ Hidden Markov Model ♦ Forwardbackward Algorithm ♦ Off-line Handwriting Recognition ♦ Postal Sorting ♦ Letter Posterior Probability Estimator ♦ Recognition System|
|Description||In Advances In Neural Information Processing Systems 8
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 forwardbackward algorithm is found to result in the recognizer with the lowest error rate. 1 Introduction In the field of off-line handwriting recognition, the goal is to read a handwritten document and produce a machine transcription. Such a system could be used for a variety of purposes, from cheque processing and postal sorting to personal correspondence reading for the blind or historical document reading. In a previous publication (Senior 1994) we have described a system based on a ...
|Educational Role||Student ♦ Teacher|
|Age Range||above 22 year|
|Education Level||UG and PG ♦ Career/Technical Study|
|Learning Resource Type||Article|
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