Optical Music Imaging

2011 ◽  
pp. 50-79 ◽  
Author(s):  
Graham Jones ◽  
Bee Ong ◽  
Ivan Bruno ◽  
Kia NG
Keyword(s):  
Image Restoration ◽  
Imaging Techniques ◽  
Recognition System ◽  
Optical Music Recognition ◽  
Cross Media ◽  
Musical Scores ◽  
Music Recognition ◽  
Potential Applications ◽  
Music Manuscripts ◽  
Media Integration

This paper presents the applications and practices in the domain of music imaging for musical scores (music sheets and music manuscripts), which include music sheet digitisation, optical music recognition (OMR) and optical music restoration. With a general background of Optical Music Recognition (OMR), the paper discusses typical obstacles in this domain and reports currently available commercial OMR software. It reports hardware and software related to music imaging, discussed the SharpEye optical music recognition system and provides an evaluation of a number of OMR systems. Besides the main focus on the transformation from images of music scores to symbolic format, this paper also discusses optical music image restoration and the application of music imaging techniques for graphical preservation and potential applications for cross-media integration.

AN OPTICAL MUSIC RECOGNITION SYSTEM FOR SKEW OR INVERTED MUSICAL SCORES

2013 ◽  
Vol 27 (07) ◽  
pp. 1353005 ◽  
Author(s):  
YUNG-SHENG CHEN ◽  
FENG-SHENG CHEN ◽  
CHIN-HUNG TENG
Keyword(s):  
Image Processing ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Pop Music ◽  
Musical Score ◽  
Optical Music Recognition ◽  
Pattern Recognition Techniques ◽  
Musical Scores ◽  
Music Recognition ◽  
Series Of Experiments

Optical Music Recognition (OMR) is a technique for converting printed musical documents into computer readable formats. In this paper, we present a simple OMR system that can perform well for ordinary musical documents such as ballad and pop music. This system is constructed based on fundamental image processing and pattern recognition techniques, thus it is easy to implement. Moreover, this system has a strong capability in skew restoration and inverted musical score detection. From a series of experiments, the error for our skew restoration is below 0.2° for any possible document rotation and the accuracy of inverted musical score detection is up to 98.89%. The overall recognition accuracy of our OMR can achieve to nearly 97%, a figure comparable with current commercial OMR software. However, if taking into image skew into consideration, our system is superior to commercial software in terms of recognition accuracy.

scholarly journals Real-Time Optical Music Recognition System for Dulcimer Musical Robot

2019 ◽  
Vol 23 (4) ◽  
pp. 782-790
Author(s):  
Zhe Xiao ◽  
Xin Chen ◽  
Li Zhou ◽  
◽  
◽  
...  
Keyword(s):  
Structural Analysis ◽  
Real Time ◽  
Electronic Music ◽  
Recognition Rate ◽  
Recognition System ◽  
Shape Model ◽  
Optical Music Recognition ◽  
Time Performance ◽  
Music Recognition ◽  
Music File

Traditional optical music recognition (OMR) is an important technology that automatically recognizes scanned paper music sheets. In this study, traditional OMR is combined with robotics, and a real-time OMR system for a dulcimer musical robot is proposed. This system gives the musical robot a stronger ability to perceive and understand music. The proposed OMR system can read music scores, and the recognized information is converted into a standard electronic music file for the dulcimer musical robot, thus achieving real-time performance. During the recognition steps, we treat note groups and isolated notes separately. Specially structured note groups are identified by primitive decomposition and structural analysis. The note groups are decomposed into three fundamental elements: note stem, note head, and note beams. Isolated music symbols are recognized based on shape model descriptors. We conduct tests on real pictures taken live by a camera. The tests show that the proposed method has a higher recognition rate.

scholarly journals A new optical music recognition system based on combined neural network

2015 ◽  
Vol 58 ◽  
pp. 1-7 ◽  
Author(s):  
Cuihong Wen ◽  
Ana Rebelo ◽  
Jing Zhang ◽  
Jaime Cardoso
Keyword(s):  
Neural Network ◽  
Recognition System ◽  
Optical Music Recognition ◽  
Music Recognition

Staff Detection and Removal

2011 ◽  
pp. 1-39 ◽  
Author(s):  
Ichiro Fujinaga
Keyword(s):  
Image Processing ◽  
Connected Component ◽  
Optical Music Recognition ◽  
Run Length ◽  
Image Processing Techniques ◽  
Run Length Coding ◽  
Musical Scores ◽  
Music Recognition ◽  
Recognition Systems ◽  
Processing Techniques

This chapter describes the issues involved in the detection and removal of stavelines of musical scores. This removal process is an important step for many Optical Music Recognition systems and facilitates the segmentation and recognition of musical symbols. The process is complicated by the fact that most music symbols are placed on top of stavelines and these lines are often neither straight nor parallel to each other. The challenge here is to remove as much of stavelines as possible while preserving the shapes of the musical symbols, which are superimposed on stavelines. Various problematic examples are illustrated and a detailed explanation of an algorithm is presented. Image processing techniques used in the algorithm include: run-length coding, connected-component analysis, and projections.

An Off-Line Optical Music Sheet Recognition

2011 ◽  
pp. 40-77 ◽  
Author(s):  
Pierfrancesco Bellini ◽  
Ivan Bruno ◽  
Paolo Nesi
Keyword(s):  
Recognition System ◽  
Music Notation ◽  
Original Music ◽  
Optical Processing ◽  
Optical Music Recognition ◽  
Digital World ◽  
Music Recognition ◽  
Critical Phase ◽  
Graphic Elements ◽  
Music Symbol

The optical music recognition is a key problem for coding music sheets of western music in the digital world. The most critical phase of the optical music recognition process is the first analysis of the image sheet. In optical processing of music or documents, the first analysis consists of segmenting the acquired sheet into smaller parts in order to recognize the basic symbols that allow reconstructing the original music symbol. In this chapter, an overview of the main issues and a survey of the main related works are discussed. The O3MR system (Object Oriented Optical Music Recognition) system is also described. The used approach in such system is based on the adoption of projections for the extraction of basic symbols that constitute graphic elements of the music notation. Algorithms and a set of examples are also included to better focus concepts and adopted solutions.

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