Using Sequence Alignment and Voting to Improve Optical Music Recognition from Multiple Recognizers

Digitalizing sheet music using Optical Music Recognition (OMR) is error-prone, especially when using noisy images created from scanned prints. Inspired by DNA-sequence alignment, we devise a method to use multiple sequence alignment to automatically compare output from multiple third party OMR tools and perform automatic error-correction of pitch and duration of notes. We perform tests on a corpus of 49 one-page scores of varying quality. Our method on average reduces the amount of errors from an ensemble of 4 commercial OMR tools. The method achieves, on average, fewer errors than each recognizer by itself, but statistical tests show that it is significantly better than only 2 of the 4 commercial recognizers. The results suggest that recognizers may be improved somewhat by sequence alignment and voting, but that more elaborate methods may be needed to obtain substantial improvements. All software, scanned music data used for testing, and experiment protocols are open source and available at: http://code.google.com/p/omr-errorcorrection/