Signal representations for hidden Markov model based online handwriting recognition

Addresses the problem of online, writer-independent, unconstrained handwriting recognition. Based on hidden Markov models (HMM), which are successfully employed in speech recognition tasks, we focus on representations which address scalability, recognition performance and compactness. `Delayed´ features are introduced which integrate more global, handwriting specific knowledge into the HMM representation. These features lead to larger error-rate reduction than `delta´ features which are known from speech recognition and even require fewer additional components. Scalability is addressed with a size-independent representation. Compactness is achieved with linear discriminant analysis. The representations are discussed and the results for a mixed-style word recognition task with vocabularies of 200 (up to 99% correct words) and 20000 words (up to 88.8% correct words) are given