Learning Prototypes for On-Line Handwritten Digits
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Online Handwritten Shape Recognition Using Segmental Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper is dedicated to data driven design method for a hybrid ANN / HMM based handwriting recognition system. On one hand, a data driven designed neural modelling of handwriting primitives is proposed. ANNs are firstly used as state models in a HMM primitivedivider that associates each signal frame with an ANN by minimizing the accumulated prediction error. Then, the neural modelling is realized by training each network on its own frame set. Organizing these two steps in an EM algorithm, precise primitive models are obtained. On the other hand, a data driven systematic method is proposed for HMM topology inference task. All possible prototypes of a pattern class are firstly merged into several clustersby a Tabu search aided clustering algorithm. Then a multiple parallel-path HMM is constructed for the pattern class. Experiments prove an 8% recognition improvement with a saving of 50% of system resources, compared to an intuitively designed referential ANN / HMM system.