Abstract
The purpose of optical music recognition is to develop a computer program that is able to understand the musical score, which is invented for human beings to annotate melody. A score is usually stored as an image. Therefore, a recognition system must retrieve musical information from a set of pixels. This dissertation deals with two major issues: preprocessing and recognition. Preprocessing aims at dividing the input image into several slices that can be processed independently and handling the defects in the printing step. The goal of preprocessing is to simplify the subsequent recognition stage. Afterward, recognition on a staff image is the core of this dissertation. The implementation is based on template matching and the support vector machine. For real score images, the present algorithm works well. The design of the present algorithm brings a different perspective to optical music recognition. First, the preprocessing uses random sample consensus (RANSAC) as a part of staff detection. Such randomness makes it meaningful to repeat the same operation; by comparing the results between different iterations, consensus-based correction provides possibility of finding symbols that other existing stable algorithms cannot find. Secondly, the algorithm is based on the divide and conquer concept, which means the subtasks have little correlation, and hence the algorithm can be readily parallelized.