A Neural-Network-Based Approach to Optical Symbol Recognition
Neural Processing Letters
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Treatment of Diagrams in Document Image Analysis
Diagrams '00 Proceedings of the First International Conference on Theory and Application of Diagrams
Defining the Syntax and Semantics of Natural Visual Languages
AGTIVE '99 Proceedings of the International Workshop on Applications of Graph Transformations with Industrial Relevance
A music notation construction engine for optical music recognition
Software—Practice & Experience
Graph transformation in document image analysis: approaches and challenges
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Grammar-based automated music composition in Haskell
Proceedings of the first ACM SIGPLAN workshop on Functional art, music, modeling & design
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This paper describes a simplified attributed programmed graph grammar to represent and process a-priori knowledge about common music notation. The presented approach serves as a high-level recognition stage and is interlocked to previous low-level recognition phases in our entire optical music recognition system (DOREMIDI++). The implemented grammar rules and control diagrams describe a declarative knowledge base to drive a transformation algorithm. This transformation converts the results of symbol recognition stages to a symbolic representation of the musical score.