Comparing adaptation techniques for on-line handwriting recognition

This paper describes an on-line handwriting recognition system with focus on adaptation techniques. Our Hidden Markov Model (HMM) -based recognition system for cursive German script can be adapted to the writing style of a new writer using either a retraining depending on the EM (expectation maximization) -approach or an adaptation according to the MAP (maximum a posteriori) or MLLR (maximum likelihood linear regression) -criterion. The performance of the resulting writer-dependent system increases significantly, even if the amount of adaptation data is very small (about 6 words). So this approach is also applicable for on-line systems in hand-held computers such as PDAs. Special attention was paid to the performance comparison of the different adaptation techniques with the availability of different amounts of adaptation data ranging from a few words up to 100 words per writer.