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Issue No.05 - May (1996 vol.18)
pp: 548-554
ABSTRACT
<p><b>Abstract</b>—A lexicon-based, handwritten word recognition system combining segmentation-free and segmentation-based techniques is described. The segmentation-free technique constructs a continuous density hidden Markov model for each lexicon string. The segmentation-based technique uses dynamic programming to match word images and strings. The combination module uses differences in classifier capabilities to achieve significantly better performance.</p>
INDEX TERMS
Hidden Markov models, dynamic programming, handwritten word recognition, character recognition, neural networks, character segmentation.
CITATION
Magdi Mohamed, Paul Gader, "Handwritten Word Recognition Using Segmentation-Free Hidden Markov Modeling and Segmentation-Based Dynamic Programming Techniques", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.18, no. 5, pp. 548-554, May 1996, doi:10.1109/34.494644
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