Recognition of Iranian accidental notes based on the zoning feature and Hough transform

This paper presents the automatic recognition of two Iranian accidental music notes from music sheets and their distinct classification from three western accidental notes. The proposed method is designed for recognizing handwritten documents it consists of four major steps, preprocessing, note extraction, feature extraction and finally classification using Artificial Neural Networks (ANNs). In the preprocessing step, the noise is removed and the thickness of staff lines is extracted then the morphological operation is used to remove staff lines. In the note extraction step, the notes are extracted from the scanned music sheets by connected component labeling. In the third step, the zoning feature and Hough transform are used to extract features. Finally at the last step, the notes are classified by the ANNs. The developed procedure was applied to the different music sheets scanned by a scanner with 300 dpi resolution and the recognition rate of accidental notes was 100%.