Comparing adaptation techniques for on-line handwriting recognition

Abstract

This paper describes an online 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 online 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 tip to 100 words per writer.