Abstract
This paper describes a recognition system for on-line cursive handwriting that requires very little initial training and that rapidly learns, and adapts to, the handwriting style of a user. Key features arc a shape analysis algorithm that determines shapes in handwritten words, a linear segmentation algorithm that matches characters identified in handwritten words to characters of candidate words, and a learning algorithm thai adapts to the user writing style. Using a lexicon with 10K words, the system achieved an average recognition rate of 81.3% for lop choice and 91.7% for the top three choices,
This work is done as part of my Ph.D. study under Professor Klaus Truemper in the AI Lab of The University of Texas at Dallas, and funded by the Office of Naval Research.
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© 1999 Springer-Verlag Berlin Heidelberg
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Qian, G. (1999). An Adaptive Handwriting Recognition System. In: Zhong, N., Skowron, A., Ohsuga, S. (eds) New Directions in Rough Sets, Data Mining, and Granular-Soft Computing. RSFDGrC 1999. Lecture Notes in Computer Science(), vol 1711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48061-7_68
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DOI: https://doi.org/10.1007/978-3-540-48061-7_68
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