Handwritten Word Recognition Using Segmentation-Free Hidden Markov Modeling and Segmentation-Based Dynamic Programming Techniques
Issue No. 05 - May (1996 vol. 18)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.494644
<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>
Hidden Markov models, dynamic programming, handwritten word recognition, character recognition, neural networks, character segmentation.
M. Mohamed and P. Gader, "Handwritten Word Recognition Using Segmentation-Free Hidden Markov Modeling and Segmentation-Based Dynamic Programming Techniques," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 18, no. , pp. 548-554, 1996.