Dynamically expanding context as committee adaptation method in on-line recognition of handwritten Latin characters

We have developed an adaptive handwriting recognizer for isolated Latin characters in which the adaptive behavior is based on the dynamically expanding context (DEC) algorithm. In our current system, the outputs of a set of static classifiers are combined in a committee machine, whose rules are adapted. Every misclassified character gives rise to adding a new DEC rule to the rule set of the committee. When the existing rules fail to produce a correct recognition output, more and more context information is utilized in forming the new DEC rules. Not only the first-ranking outputs from the member classifiers but also the second-ranking ones can be taken into account when forming the DEC rules. In the experiments described in this paper, various options in the implementation of the DEC committee classifier are evaluated. The results of the experiments show that the system is capable of fast adaptation to the user´s handwriting and lead to lowered recognition error rates