Staff line restoration

Optical music recognition (OMR), the conversion of scanned pages of music into a musical database, has reached an exciting level of maturity. Like optical character recognition, it has now reached the point where the returns in accuracy from increasingly sophisticated pattern recognition algorithms appears saturated and more significant gains are being made from the application of structured a priori knowledge. This paper describes one such technique for improved staff line processing-the detection and subsequent correction of bowing in the staff lines, which is an important category given the significant source of music in book form. Two versions of the algorithm are tested: the first, based on mathematical morphology, has the added benefit of automatically fusing small breaks in staff lines, common for example in older works; the second, based on a flood-fill algorithm, requires a minor modification if fragmented staff lines are to be repaired. The correct detection and processing of staff lines is fundamental to OMR. Without adequate knowledge of staff line location, notation superimposed on the staves cannot be correctly separated, classified and processed.