손사보 악보의 광학음악인식을 위한 CNN 기반의 보표 및 마디 인식

• Published in: 한국정보통신학회논문지 Vol. 26; no. 7; pp. 1098 - 1101
• Main Authors: 박종원(Jong-Won Park), 김동삼(Dong-Sam Kim), 김준호(Jun-Ho Kim)
• Format: Journal Article
• Language: Korean
• Published: 한국정보통신학회 2022
• Subjects: 전자/정보통신공학; Staff-line detection; Convolutional Neural Network; Measure detection; Handwritten music sheet
• ISSN: 2234-4772; 2288-4165
• DOI: 10.6109/jkiice.2022.26.7.1098

With the development of computer music notation programs, when drawing sheet music, it is often drawn using a computer. However, there are still many use of hand-written notations for educational purposes or to quickly draw sheet music such as listening and dictating. In previous studies, OMR focused on recognizing the printed music sheet made by music notation program. the result of handwritten OMR with camera is poor because different people have different writing methods, and lens distortion. In this study, as a pre-processing process for recognizing handwritten music sheet, we propose a method for recognizing a staff using linear regression and a method for recognizing a bar using CNN. F1 scores of staff recognition and barline detection are 99.09% and 95.48%, respectively. This methodologies are expected to contribute to improving the accuracy of handwriting.