NDLI: Improved on-line handwriting recognition using context dependent hidden Markov models

Content Provider	IEEE Xplore Digital Library
Author	Kosmala, A. Rottland, J. Rigoll, G.
Copyright Year	1997
Description	Author affiliation: Dept. of Comput. Sci., Gerhard-Mercator-Univ., Diusburg, Germany (Kosmala, A.)
Abstract	The paper presents the introduction of context dependent hidden Markov models for cursive, unconstrained handwriting recognition with large vocabularies. Since context dependent models were successfully introduced to speech recognition (R. Bahl et al., 1980; R. Schwartz et al., 1984; K. Lee, 1990), it seems obvious, that the use of trigraphs could also lead to improved online handwriting recognition systems (A. Kosmala et al., 1997). In analogy to triphones in speech recognition, trigraphs are context dependent sub word units representing a single written character in its left and right context. The tests were conducted on a writer dependent system with three different writers and two different vocabulary sizes (1000 words and 30000 words). The results we obtained with the trigaph based system compared to the monograph system, are very encouraging: a mean relative error reduction of 46% for the 1000 word handwriting recognition system and a mean relative error reduction of 37% for the same system with the 30000 word vocabulary. We believe that this represents one of the first systematic investigations of the influence of context dependent models and parameter reduction methods for a difficult large vocabulary handwriting recognition task.
Starting Page	641
Ending Page	644
File Size	384124
Page Count	4
File Format	PDF
ISBN	0818678984
DOI	10.1109/ICDAR.1997.620584
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-08-18
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Handwriting recognition Hidden Markov models Vocabulary Writing Context modeling Speech recognition System testing Computer science Character recognition Robustness
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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