The main goal of the work presented in this thesis has been the development of an on-line handwriting recognition system which is able to recognize handwritten characters of several different writing styles and is able to improve its performance by adapting itself to new writing styles. The recognition method should be applicable to hand-held devices of limited memory and computational resources. The adaptation process should take place during normal use of the device, not in some specific training mode. For the usability aspect of the recognition system, the recognition and adaptation processes should be easily understandable to the users.The first part of this thesis gives an introduction to the handwriting recognition. The topics considered include: the variations present in personal handwriting styles; automatic grouping of similar handwriting styles; the differences between writer-independent and writer-dependent as well as on-line and off-line handwriting recognition problems; the different approaches to on-line handwriting recognition; the previous adaptive recognition systems and the experiments performed with them; the recognition performance requirements and other usability issues related to on-line handwriting recognition; the current trends in on-line handwriting recognition research; the recognition results obtained with the most recent recognition systems; and the commercial applications.The second part of the thesis describes an adaptive on-line character recognition system and the experiments performed with it. The recognition system is based on prototype matching. The comparisons between the character samples and prototypes are based on the Dynamical Time Warping (DTW) algorithm and the input characters are classified according to the k Nearest Neighbors (k-NN) rule. The initial prototype set is formed by clustering character samples collected from a large number of subjects. Thus, the recognition system can handle various writing styles. This thesis work introduces four DTW-based clustering algorithms which can be used for the prototype selection. The recognition system adapts to new writing styles by modifying its prototype set. This work introduces several adaptation strategies which add new writer-dependent prototypes into the initial writer-independent prototype set, reshape the existing prototypes with a Learning Vector Quantization (LVQ)-based algorithm, and inactivate poorly performing prototypes. The adaptations are carried out on-line in a supervised or self-supervised fashion. In the former case, the user explicitly labels the input characters which are used as training samples in the adaptation process. In the latter case, the system deduces the labels from the recognition results and the user's actions. The latter approach is prone to erroneously labeled learning samples.The different adaptation strategies were experimented with and compared with each other by performing off-line simulations and genuine on-line user experiments. In the simulations, special attention has been paid to the various erroneous learning situations likely to be encountered in real world handwriting recognition tasks. The recognition system is able to improve its recognition accuracy significantly on the basis of only a few additional character samples per class. Recognition accuracies acceptable in real world applications can be attained for most of the test subjects.This work also introduces a Self-Organizing Map (SOM)-based method for analyzing personal writing styles. Personal writing styles are represented by high-dimensional vectors, the components of which indicate the subjects' tendencies to use certain prototypical writing styles for isolated characters. These writing style vectors are then visualized by a SOM which enables the detection and analysis of clusters of similar writing styles.
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