Sheet Music Statistical Layout Analysis
In order to provide access to the contents of ancient music scores to researchers, the transcripts of both the lyrics and the musical notation is required. Before attempting any type of automatic or semi automatic transcription of sheet music, an adequate layout analysis (LA) is needed. This LA must provide not only the locations of the different image regions, but also adequate region labels to distinguish between different region types such as staff, lyric, etc. To this end, we adapt a stochastic framework for LA based on Hidden Markov Models that we had previously introduced for detection and classification of text lines in typical handwritten text images. The proposed approach takes a scanned music score image as input and, after basic preprocessing, simultaneously performs region detection and region classification in an integrated way. To assess this statistical LA approach several experiments were carried out on a representative sample of a historical music archive, under different difficulty settings. The results show that our approach is able to tackle these structured documents providing good results not only for region detection but also for classification of the different regions.
2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)