Optical Music Recognition using Image Processing and Machine Learning

Abstract

The ability to understand music score is a basic requirement for learning music. This paper proposes a mathematical method to find the pitch of a musical note from digital image of sheet music and a classification-based method for detecting the duration of a music note. In a sheet music, the horizontal direction can be associated with the notes starting time, whilst the vertical direction can be associated with pitch. The symbols used for a note represents its duration. Music scores sometimes need to be transposed or slightly modified, having the score in a digital format greatly reduces the time and effort required to do these. In this paper, we make use of techniques such as Run Length Encoding (RLE), Horizontal projection and Vertical Projection (X & Y projections) for Segmentation and attribute extraction. For note recognition, a classifier based system is used which returns the duration of the given input symbol. The pitch, duration and position of notes are finally given as input to a midi generation module, which generates a MIDI file corresponding to the given input music notation. There are several other applications to Optical Music Recognition (OMR) systems. Converting music scores in Braille code for the blind is yet another application of an OMR system