Novel VQ Designs for Discrete HMM On-Line Handwritten Whiteboard Note Recognition
Proceedings of the 30th DAGM symposium on Pattern Recognition
Novel VQ with constraints on the quantization error distribution
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Harmonic and instrumental information fusion for musical genre classification
Proceedings of 3rd international workshop on Machine learning and music
Assamese online handwritten digit recognition system using hidden Markov models
Proceeding of the workshop on Document Analysis and Recognition
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In this paper we present a new multiple classifier system (MCS) for recognizing notes written on a whiteboard. This MCS combines one off-line and two on-line handwriting recognition systems derived from previous work. The rec- ognizers are all based on Hidden Markov Models but vary in the way of preprocessing and normalization. To combine the output sequences of the recognizers, we incrementally align the word sequences using a standard string matching algorithm. For deriving the final decision a voting strat- egy is applied. With the combination we could increase the system performance over the best individual recognizer by about 2%.