Name
Abstract | ||
|---|---|---|
Optical music recognition (OMR), when the input music score is captured by a handheld or a mobile phone camera, suffers from severe degradation in the image quality and distortions caused by non-planar document curvature and perspective projection. Hence the binarization of the input often fails to preserve the details of the original music score, leading to a poor performance in recognition of music symbols. This paper addresses the issue of staff line detection, which is the most important step in OMR, in the presence of nonlinear distortions and describes how to cope with severe degradations in recognition of music symbols. First, a RANSAC-based detection of curved staff lines is presented and staves are segmented into sub-areas for the rectification with bi-quadratic transformation. Then, run length coding is used to recognize music symbols such as stem, note head, flag, and beam. The proposed system is implemented on smart phones, and it shows promising results with music score images captured in the mobile environment. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s11042-017-5169-9 | Multimedia Tools Appl. |
Keywords | Field | DocType |
Music score recognition, Optical music recognition, Staff line removal, Staff line detection | Optical music recognition,Computer vision,Nonlinear system,Computer science,RANSAC,Image quality,Perspective (graphical),Speech recognition,Run length coding,Mobile device,Artificial intelligence,Mobile phone camera | Journal |
Volume | Issue | ISSN |
77 | 12 | 1380-7501 |
Citations | PageRank | References |
1 | 0.34 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Quang Nhat Vo | 1 | 1 | 1.35 |
| Gueesang Lee | 2 | 208 | 52.71 |
| Soo-Hyung Kim | 3 | 191 | 49.03 |
| Hyungjeong Yang | 4 | 455 | 47.05 |