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Handwritten Word Recognition Using Segmentation-Free Hidden Markov Modeling and Segmentation-Based Dynamic Programming Techniques
May 1996 (vol. 18 no. 5)
pp. 548-554

Abstract—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.

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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 and Machine Intelligence, vol. 18, no. 5, pp. 548-554, May 1996, doi:10.1109/34.494644
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