Optical music recognition (OMR) enables early music collections to be digitized on a large scale. The workflow for such digitisation projects also includes scanning and preprocessing, but the cost of expert human labour to correct automatic recognition errors dominates the cost of these other two steps. To reduce the number of recognition errors in the OMR process, we present an innovative application of maximum a posteriori (MAP) adaptation for hidden Markov models (HMMs) to build book-adaptive models, taking advantage of the new learning data generated from human editing work, which is part of any music digitization project. We also experimented with using the generated data to build book-dependent models from scratch, which sometimes outperform the book-adaptive models after enough corrected data is available. Our experiments show that these approaches can reduce human editing costs by more than half and that they are especially well suited to highly variable sources like early or degraded documents.