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.

Optical Music Recognition

2011 ◽  
pp. 80-110 ◽  
Author(s):  
Pierfrancesco Bellini ◽  
Ivan Bruno ◽  
Paolo Nesi
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Research And Development ◽  
European Commission ◽  
Image Understanding ◽  
Development Project ◽  
Music Notation ◽  
Optical Music Recognition ◽  
Digital World ◽  
Music Recognition

Optical music recognition is a key problem for coding western music sheets in the digital world. This problem has been addressed in several manners obtaining suitable results only when simple music constructs are processed. To this end, several different strategies have been followed, to pass from the simple music sheet image to a complete and consistent representation of music notation symbols (symbolic music notation or representation). Typically, image processing, pattern recognition and symbolic reconstruction are the technologies that have to be considered and applied in several manners the architecture of the so called OMR (Optical Music Recognition) systems. In this chapter, the O3MR (Object Oriented Optical Music Recognition) system is presented. It allows producing from the image of a music sheet the symbolic representation and save it in XML format (WEDELMUSIC XML and MUSICXML). The algorithms used in this process are those of the image processing, image segmentation, neural network pattern recognition, and symbolic reconstruction and reasoning. Most of the solutions can be applied in other field of image understanding. The development of the O3MR solution with all its algorithms has been partially supported by the European Commission, in the IMUTUS Research and Development project, while the related music notation editor has been partially funded by the research and development WEDELMUSIC project of the European Commission. The paper also includes a methodology for the assessment of other OMR systems. The set of metrics proposed has been used to assess the quality of results produce by the O3MR with respect the best OMR on market.

Pengantar dan Survey Tentang Optical Music Recognition

2014 ◽  
Vol 6 (1) ◽  
pp. 36-39
Author(s):  
Kevin Purwito
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Optical Music Recognition ◽  
Optical Character ◽  
Digital Format ◽  
Music Recognition ◽  
Index Terms ◽  
The Many ◽  
Further Development ◽  
Music Symbol

This paper describes about one of the many extension of Optical Character Recognition (OCR), that is Optical Music Recognition (OMR). OMR is used to recognize musical sheets into digital format, such as MIDI or MusicXML. There are many musical symbols that usually used in musical sheets and therefore needs to be recognized by OMR, such as staff; treble, bass, alto and tenor clef; sharp, flat and natural; beams, staccato, staccatissimo, dynamic, tenuto, marcato, stopped note, harmonic and fermata; notes; rests; ties and slurs; and also mordent and turn. OMR usually has four main processes, namely Preprocessing, Music Symbol Recognition, Musical Notation Reconstruction and Final Representation Construction. Each of those four main processes uses different methods and algorithms and each of those processes still needs further development and research. There are already many application that uses OMR to date, but none gives the perfect result. Therefore, besides the development and research for each OMR process, there is also a need to a development and research for combined recognizer, that combines the results from different OMR application to increase the final result’s accuracy. Index Terms—Music, optical character recognition, optical music recognition, musical symbol, image processing, combined recognizer  

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.

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.

Optical Music Recognition with Wavelets

2005 ◽  
pp. 2225-2229
Author(s):  
Susan E. George
Keyword(s):  
Wavelet Transform ◽  
General Purpose ◽  
Music Notation ◽  
Optical Music Recognition ◽  
Music Recognition ◽  
Logical Representation ◽  
High Level

The aim of optical music recognition (OMR) is to “recognise” images of music notation and capture the “meaning” of the music. When OMR is successful it will be able to automatically extract a logical representation of printed or handwritten music captured in an image. There are a variety of reasons why OMR is required. Chiefly, it is convenient for swift input of music notation and might be subsequently edited, performed, used as a search or other. There are many stages before that final high-level interpretation can be made and recognition of the primitive symbols contained in the notation is primary. One of the biggest challenges in OMR is the super-imposition of music notation symbols – notes and other – upon stave lines in the music image. This article examines a general-purpose knowledge-free method in the wavelet transform, to deal with super-imposition in images of typeset music.

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 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
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