The research literature concerning the automatic analysis of images of printed and handwritten music notation, for the period 1966 through 1990, is surveyed and critically examined.

A Critical Survey of Music Image Analysis

A personal imaging computer system, which is connectable to and operable with a computerized local or wide area network, identifies characters in a document on which the characters are formed. The system scans the document to obtain a gray-scale image of the document, de-skews the gray-scale image, generates a binary image from the de-skewed gray-scale image by comparing the gray-scale image with the threshold, segments the binary image to locate individual characters within the binary image and to determine the shape of the individual characters, extracts gray-scale image information from the gray-scale image for each such individual character based on the location and shape of the character in the binary image, recognition-processes the extracted gray scale image information to determine the identity of the character, and stores the identity of the character. Image pre-processing for the personal imaging computer system includes pre-processing for de-skewing the image, for obtaining and applying a global threshold which converts a gray-scale image to a binary image, for removing underlines from underlined characters in the image, for obtaining connected components within a binary image, and for applying plural sets of rules to the connected components so as to filter text-type connected components from non-text type connected components, whereby only text-type connected components are subjected to character recognition processing.

Image pre-processor for character recognition system

The basic goal of the Adaptive Optical Music Recognition system presented herein is to create an adaptive software for the recognition of musical notation. The focus of this research has been to create a robust framework upon which a practical optical music recognizer can be built.
The strength of this system is its ability to learn new music symbols and handwritten notations. It also continually improves its accuracy in recognizing these objects by adjusting internal parameters. Given the wide range of music notation styles, these are essential characteristics of a music recognizer.
The implementation of the adaptive system is based on exemplar-based incremental learning, analogous to the idea of "learning by example," that identifies unknown objects by their similarity to one or more of the known stored examples. The entire process is based on two simple, yet powerful algorithms: k-nearest neighbour classifier and genetic algorithm. Using these algorithms, the system is designed to increase its accuracy over time as more data are processed.

M.P. Thorne

Papers

Using a micro to automate data acquisition in music publishing

Coping with some really rotten problems in automatic music recognition