科研之友
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摘要
Optical Music Recognition (OMR) can be divided into three main phases: (i) staff line detection and removal. The goal of this phase is to detect and to remove staff lines from sheet music images. (ii) music symbol detection and segmentation. The propose of this phase is to detect the remaining musical symbols such as single symbols and group symbols, then segment the group symbols to single or primitive symbols after removing staff lines. (iii) musical symbols recognition. In this phase, recognition of musical symbols is the main objective. The method presented in this paper, covers all three phases. One advantage of the first phase of the proposed method is that it is robust to staff lines rotation and staff lines which have curvature in sheet music images. Moreover, the staff lines are removed accurately and quickly and also fewer details of the musical symbols are omitted. The proposed method in the first phase focuses on the hand-written documents databases which have been introduced in the CVC-MUSCIMA and ICDAR 2013. It has the lowest error rate among well-known methods and outperforms the state of the art in CVC-MUSCIMA database. In ICDAR 2013, the specificity measure of this method is 99.71% which is the highest specificity among available methods. Also, in terms of accuracy, recall rate and f-measure is only slightly less than the best method. Therefor our method is comparable favorably to the existing methods. In the second phase, the symbols are divided into two categories, single and group. In the recognition phase, we use a pattern matching method to identify single symbols. For recognizing group symbols, a hierarchical method is proposed. The proposed method in the third phase has several advantages over the previous methods. It is quite robust to skewness of musical group symbols. Furthermore, it provides high accuracy in recognition of the symbols.
- 出版日期2018-7
