Goal-Directed Evaluation for the Improvement of Optical Music Recognition on Early Music Prints

Optical music recognition (OMR) systems are promising tools for the creation of searchable digital music libraries. Using art adaptive OMR system for early music prints based oil hidden Markov models, we leverage an edit-distance evaluation metric to improve recognition accuracy. Baseline results are computed with new labeled training and test sets drawn from a diverse group of prints. We present, two experiments based oil this evaluation technique. The first resulted in a significant improvement to the feature extraction function for these images. The second is a, goal-directed comparison of several popular adaptive binarization algorithms, which are often evaluated only subjectively. Accuracy increased by as much as 55% for some pages, and the experiments suggest several avenues for further research.