A Neural-Network-Based Approach to Optical Symbol Recognition
Neural Processing Letters
A Symbol Classifier Able to Reject Wrong Shapes for Document Recognition Systems
GREC '99 Selected Papers from the Third International Workshop on Graphics Recognition, Recent Advances
Using a Generic Document Recognition Method for Mathematical Formulae Recognition
GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
Assessing Optical Music Recognition Tools
Computer Music Journal
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Optical Music Recognition is a form of document analysis for which a priori knowledge is particularly important. Musical notation is governed by a substantial set of rules, but current systems fail to use them adequately. In complex scores, existing systems cannot overcome the well-known segmentation problems of document analysis, due mainly to the high density of music information. This paper proposes a new method of recognition which uses a grammar in order to formalize the syntactic rules and represent the context. However, where objects touch, there is a discrepancy between the way the existing knowledge (grammar) will describe an object and the way it is recognized, since touching objects have to be segmented first. Following a description of the grammar, this paper shall go on to propose the use of an operator to modify the way the grammar parses the image so that the system can deal with certain touching objects (e.g. where an accidental touches a notehead).