A Stave-Aware Optical Music Recognition on Monophonic Scores for Camera-Based Scenarios

Abstract

The recognition of printed music sheets in camera-based realistic scenarios is a novel research branch of optical music recognition (OMR). However, special factors in realistic scenarios, such as uneven lighting distribution and curvature of staff lines, can have adverse effects on OMR models designed for digital music scores. This paper proposes a stave-aware method based on object detection to recognize monophonic printed sheet music in camera-based scenarios. By detecting the positions of staff lines, we improve the accuracy of note pitch effectively. In addition, we present the Camera Printed Music Staves (CPMS) dataset, which consists of labels and images captured by mobile phones under different angles and lighting conditions in realistic scenarios. We compare our method after training on different datasets with a sequence recognition method called CRNN-CTC on the test set of the CPMS dataset. The results show that the accuracy, robustness, and data dependency of our method perform better.