NDLI: Comparing normalization and adaptation techniques for online handwriting recognition

Content Provider	IEEE Xplore Digital Library
Author	Brakensiek, A. Kosmala, A. Rigoll, G.
Copyright Year	2002
Description	Author affiliation: Dept. of Comput. Sci. & Electr. Eng., Gerhard Mercator Univ., Duisburg, Germany (Brakensiek, A.; Kosmala, A.)
Abstract	In this paper a writer-independent online handwriting recognition system is described comparing the influence of handwriting normalization and adaptation techniques on the recognition performance. Our hidden Markov model-based recognition system for unconstrained German script can be adapted to the writing style of a new writer using different adaptation techniques whereas the impact of preprocessing to normalize the pen-trajectory is examined. The performance of the resulting writer-dependent system increases significantly, even if only a few words are available for adaptation. So this approach is also applicable for online systems in hand-held computers such as PDAs. In addition, the developed normalization techniques are helpful to improve completely writer independent systems. This paper presents the performance comparison of three different adaptation techniques either in a supervised or unsupervised mode, in combination with appropriate normalization methods, with the availability of different amount of adaptation data ranging from only 6 words up to 100 words per writer.
Starting Page	73
Ending Page	76
File Size	298807
Page Count	4
File Format	PDF
ISBN	076951695X
ISSN	10514651
DOI	10.1109/ICPR.2002.1047798
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-08-11
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Handwriting recognition Writing Hidden Markov models Error analysis Databases Computer science Man machine systems Personal digital assistants Books Application software
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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