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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Hybrid HMM/ANN Models for Bimodal Online and Offline Cursive Word Recognition
verfasst von : S. España-Boquera, J. Gorbe-Moya, F. Zamora-Martínez, M. J. Castro-Bleda
Erschienen in: Recognizing Patterns in Signals, Speech, Images and Videos
Verlag: Springer Berlin Heidelberg
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The recognition performance of current automatic offline handwriting transcription systems is far from being perfect. This is the reason why there is a growing interest in assisted transcription systems, which are more efficient than correcting by hand an automatic transcription. A recent approach to interactive transcription involves multi-modal recognition, where the user can supply an online transcription of some of the words. In this paper, a description of the bimodal engine, which entered the “Bi-modal Handwritten Text Recognition” contest organized during the 2010 ICPR, is presented. The proposed recognition system uses Hidden Markov Models hybridized with neural networks (HMM/ANN) for both offline and online input. The
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-best word hypothesis scores for both the offline and the online samples are combined using a log-linear combination, achieving very satisfying results.