Improved Optical Music Recognition (OMR) Justin Greet Stanford University
[email protected] Abstract same index in S. Observe that the order of the notes is implicitly mea- This project focuses on identifying and ordering the sured. Any note missing from the output, out of place, or notes and rests in a given measure of sheet music using a erroneously added heavily penalizes the percentage. More novel approach involving a Convolutional Neural Network qualitatively, we can render the output of the algorithm as (CNN). Past efforts in the field of Optical Music Recogni- sheet music and visually check how it compares to the input tion (OMR) have not taken advantage of neural networks. measure. The best architecture we developed involves feeding pro- Multiple commercial OMR products exist and their ef- posals of regions containing notes or rests to a CNN then fectiveness has been studied to be imperfect enough to pre- removing conflicts in regions identifying the same symbol. clude many practical applications [4, 20]. We run the test We compare our results with a commercial OMR product images through one of them (SmartScore [12]) and use the and achieve similar results. evaluation criteria described above to serve as a benchmark for the proposed algorithm. 1. Introduction 2. Related Work Optical Music Recognition (OMR) is the problem of The topic of OMR is heavily researched. The research converting a scanned image of sheet music into a symbolic can be broadly broken up into three categories: evaluation representation like MusicXML [9] or MIDI. There are many of existing methods, discovery of novel solutions for spe- obvious practical applications for such a solution, such as cific parts of OMR, and classical approaches to OMR.