Pen-Based Input for On-Line Handwritten Notation

Pen-Based Input for On-Line Handwritten Notation

Susan E. George (University of South Australia, Australia)
DOI: 10.4018/978-1-59140-298-5.ch005


This chapter is concerned with a novel pen-based interface for (handwritten) music notation. The chapter makes a survey of the current scope of on-line (or dynamic) handwritten input of music notation, presenting the outstanding problems in recognition. A solution using the multi-layer perceptron artificial neural network is presented explaining experiments in music symbol recognition from a study involving notation writing from some 25 people using a pressure-sensitive digitiser for input. Results suggest that a voting system among networks trained to recognize individual symbols produces the best recognition rate in the order of 92% for correctly recognising a positive example of a symbol and 98% in correctly rejecting a negative example of the symbol. A discussion is made of how this approach can be used in an interface for a pen-based music editor. The motivation for this chapter includes (i) the practical need for a pen-based interface capable of recognizing unconstrained handwritten music notation, (ii) the theoretical challenges that such a task presents for pattern recognition and (iii) the outstanding neglect of this topic in both academic and commercial respects.

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