Knowledge-based scribe recognition in historical music archives

Abstract

For the content-based management and access to domain-specific data in digital libraries, special domain-knowledge and knowledge processing functionality are required. However, the integration of knowledge components has not yet become an integral part of existing digital library systems. The current paper represents the realization of a digital archive of historical music scores, integrating special domain-specific data and functionality for writer identification in historical music scores. We introduce the basic formalisms and heuristics for the representation of handwriting characteristics. To compare two handwritings we propose the usage of a normalized, weighted Hamming distance function to calculate the degree of similarity between their handwriting characteristics. For the identification of writers we employ the k-nearest neighbor method to build clusters of similar writers, based on the calculated distance. And finally, we represent and evaluate the test results from the prototype implementation of the system. © Springer-Verlag Berlin Heidelberg 2004.