Stave Extraction for Printed Music Scores
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
A Comparative Study of Staff Removal Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Staff Detection with Stable Paths
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Staffline Thickness and Distance Estimation in Binary and Gray-Level Music Scores
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
An Efficient Staff Removal Approach from Printed Musical Documents
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Music score binarization based on domain knowledge
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
The ICDAR 2011 Music Scores Competition: Staff Removal and Writer Identification
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
An Effective Staff Detection and Removal Technique for Musical Documents
DAS '12 Proceedings of the 2012 10th IAPR International Workshop on Document Analysis Systems
| Hi-index | 0.00 |
Lately, there is an increased interest in the analysis of music score facsimiles, aiming at automatic digitization and recognition. Noise, corruption, variations in handwriting, non-standard page layouts and notations are common problems affecting especially the centuries-old manuscripts. Starting from a facsimile, the current state-of-the-art methods binarize the image, detect and group the staff lines, then remove the staff lines and classify the remaining symbols imposing rules and prior knowledge to obtain the final digital representation. The first steps are critical for the performance of the overall system. Here we propose to handle binarization, staff detection and noise removal by means of dynamic programming (DP) formulations. Our main insights are: a) the staves (the 5-groups of staff lines) are represented by repetitive line patterns, are more constrained and informative, and thus we propose direct optimization over such patterns instead of first spotting single staff lines, b) the optimal binarization threshold also is the one giving the maximum evidence for the presence of staves, c) the noise, or background, is given by the regions where there is insufficient stave pattern evidence. We validate our techniques on the CVC-MUSCIMA(2011) staff removal benchmark, achieving the best error rates (1.7%), as well as on various, other handwritten score facsimiles from the Renaissance.