Experiments with Adaptation Methods in On-line Recognition of Isolated Latin Characters

The purpose of this paper is to summarize our work on adaptive on-line recognition methods for handwritten characters. Reports on the work have been published in various conference proceedings and book chapters. As each publication covers only some specific part of our work, it is hard to see the whole picture and get a good overview of the whole work. Instead of trying to explain in detail all the techniques and experiments, we compare them with each other and give more general results. By adaptation we mean that the system is able to learn new writing styles and thus improve its performance. We have had two different approaches to the adaptation: experiments have been carried out with both individually adaptive classifiers and adaptive committees of static classifiers. The main techniques applied in our work include the k-Nearest Neighbor and the Local Subspace Classification rules, Dynamic Time Warping and Levenshtein distances, Learning Vector Quantization, and Dynamically Expanding Context.