Signal representations for hidden Markov model based online handwriting recognition

This paper addresses the problem of on-line, 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 i n tegrate more global, handwriting specic 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 i s addressed with a size-independent representation. Compactness is achieved with Linear Discriminant Analysis (LDA). The representations are discussed and the results for a mixed-style word recognition task with vocabularies of 200 (up to 99% correct words) and 20,000 words (up to 88.8% correct words) are given.