Comparing normalization and adaptation techniques for online handwriting recognition

In this paper a writer-independent online handwriting recognition system is described comparing the influence of handwriting normalization and adaptation techniques on the recognition performance. Our hidden Markov model-based recognition system for unconstrained German script can be adapted to the writing style of a new writer using different adaptation techniques whereas the impact of preprocessing to normalize the pen-trajectory is examined. The performance of the resulting writer-dependent system increases significantly, even if only a few words are available for adaptation. So this approach is also applicable for online systems in hand-held computers such as PDAs. In addition, the developed normalization techniques are helpful to improve completely writer independent systems. This paper presents the performance comparison of three different adaptation techniques either in a supervised or unsupervised mode, in combination with appropriate normalization methods, with the availability of different amount of adaptation data ranging from only 6 words up to 100 words per writer.