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The paper deals with optical music recognition (OMR) as a process of structured data processing applied to music notation. Granularity of OMR in both its aspects: data representation and data processing is especially emphasised in the paper. OMR is a challenge in intelligent computing technologies, especially in such fields as pattern recognition and knowledge representation and processing. Music notation is a language allowing for communication in music, one of most sophisticated field of human activity, and has a high level of complexity itself. On the one hand, music notation symbols vary in size and have complex shapes; they often touch and overlap each other. This feature makes the recognition of music symbols a very difficult and complicated task. On the other hand, music notation is a two dimensional language in which importance of geometrical and logical relations between its symbols may be compared to the importance of the symbols alone. Due to complexity of music nature and music notation, music representation, necessary to store and reuse recognised information, is also the key issue in music notation recognition and music processing. Both: the data representation and the data processing used in OMR is highly structured, granular rather than numeric. OMR technology fits paradigm of granular computing