The objective of this study is to produce a system that would allow music symbols to be written by hand using a pen-based computer that would simulate the feeling of writing on sheets of paper and that would also accurately recognize the music symbols. To accomplish these objectives, the following methods are proposed: (1) Two features, time-series data and an image of a handwritten stroke, are used to recognize strokes; and (2) The strokes are combined, as efficiently as possible, and outputted automatically as a music symbol. As a result, recognition rates of 97.60% and 98.80% were obtained in tests with strokes and music symbols, respectively.
This paper proposes extraction composed of the following four processes to accurately extract the position of a stave, including inclination, break, and flexion: (1) Find stave candidate points on a scanning line when an image is scanned vertically at equally spaced intervals. (2) Use DP matching to link stave candidate points. (3) Use labeling to separate them into a group for each stave. (4) Extract end points of a stave and correct their positions. When this extraction was applied to 104 printed music scores, it accurately extracted 99.52% of stave positions.
This paper proposes a method to improve off-line character classifiers learned from examples using virtual examples synthesized from an on-line character database. To obtain good classifiers, a large database which contains a large enough number of variations of handwritten characters is usually required. However, in practice, collecting enough data is time-consuming and costly. In this paper, we propose a method to train SVM for off-line character recognition based on artificially augmented examples using on-line characters. In our method, virtual examples are synthesized from on-line characters by the following two steps: (1) applying affine transformation to each stroke of "real" characters, and (2) applying affine transformation to each stroke of artificial characters, which are synthesized on the basis of PCA. SVM classifiers are trained by using the training samples containing artificially generated patterns and real characters. We examine the effectiveness of the proposed method with respect to the recognition rates and number of support vectors of SVM through experiments involving the handwritten Japanese Hiragana character classification.
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