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The Writer Independent Online Handwriting Recognition System frog on hand and Cluster

Summary: The Writer Independent Online Handwriting
Recognition System frog on hand and Cluster
Generative Statistical Dynamic Time Warping
Claus Bahlmann and Hans Burkhardt, Member, IEEE
Abstract--In this paper, we give a comprehensive description of our writer-independent online handwriting recognition system frog on
hand. The focus of this work concerns the presentation of the classification/training approach, which we call cluster generative statistical
dynamic time warping (CSDTW). CSDTW is a general, scalable, HMM-based method for variable-sized, sequential data that holistically
combines cluster analysis and statistical sequence modeling. It can handle general classification problems that rely on this sequential
type of data, e.g., speech recognition, genome processing, robotics, etc. Contrary to previous attempts, clustering and statistical
sequence modeling are embedded in a single feature space and use a closely related distance measure. We show character recognition
experiments of frog on hand using CSDTW on the UNIPEN online handwriting database. The recognition accuracy is significantly higher
than reported results of other handwriting recognition systems. Finally, we describe the real-time implementation of frog on hand on a
Linux Compaq iPAQ embedded device.
Index Terms--Pattern recognition, handwriting analysis, Markov processes, dynamic programming, clustering.
DURING recent years, the task of online handwriting
recognition (HWR) has gained an immense importance
in every day applications, mainly due to the increasing
popularity of the personal digital assistant (PDA). Currently,


Source: Albert-Ludwigs-Universität Freiburg, Institut für Informatik,, Lehrstuhls für Mustererkennung und Bildverarbeitung


Collections: Computer Technologies and Information Sciences