On-Line Cursive Script Recognition using Time Delay Neural Networks and Hidden Markov Models (0)
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BibTeX
@MISC{Schenkel_on-linecursive,
author = {M. Schenkel and I. Guyon and D. Henderson},
title = {On-Line Cursive Script Recognition using Time Delay Neural Networks and Hidden Markov Models},
year = {}
}
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Abstract
We present a writer independent system for on-line handwriting recognition which can handle a variety of writing styles including cursive script and hand-print. The input to our system contains the pen trajectory information, encoded as a time-ordered sequence of feature vectors. A Time Delay Neural Network is used to estimate a posteriori probabilities for characters in a word. A Hidden Markov Model segments the word in a way which optimizes the global word score, taking a dictionary into account. A geometrical normalization scheme and a fast but efficient dictionary search are also presented. Trained on 20k words from 59 writers, using a 25k word dictionary we reached a 89% character and 80% word recognition rate on test data from a disjoint set of writers. Keywords: Handwriting Recognition, Neural Networks, Cursive Script, Hidden Markov Models, Dictionary Search. 1 Introduction Pen interfaces should replace advantageously both mouse and keyboard in a variety of situations. Users w...
