Staff line detection and removal with stable paths

• Type: Article in International Conference Proceedings Book
• Date: 2008

Authors

Artur Capela (Author) Other; Ana Rebelo (Author) Other; Jaime S Cardoso (Author) FEUP; Carlos Guedes (Author) Other

Conference proceedings International

Title: SIGMAP 2008: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND MULTIMEDIA APPLICATIONS

Pages: 263-270

International Conference on Signal Processing and Multimedia Applications

Oproto, PORTUGAL, JUL 26, 2008

Indexing

ISI Web of Knowledge - 3 Citations

Scopus - 5 Citations

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

Other information

Authenticus ID: P-004-4V7

Abstract (EN): Many music works produced in the past are currently available only as original manuscripts or as photocopies. Preserving them entails their digitalization and consequent accessibility in a machine-readable format, which encourages browsing, retrieval, search and analysis while providing a generalized access to the digital material. Carrying this task manually is very time consuming and error prone. While optical music recognition (OMR) systems usually perform well on printed scores, the processing of handwritten music by computers remains below the expectations. One of the fundamental stages to carry out this task is the detection and subsequent removal of staff lines. In this paper we integrate a general-purpose, knowledge-free method for the automatic detection of staff lines based on stable paths, into a recently developed staff line removal toolkit. Lines affected by curvature, discontinuities, and inclination are robustly detected. We have also developed a staff removal algorithm adapting an existing line removal approach to use the stable path algorithm at the detection stage, Experimental results show that the proposed technique outperforms well-established algorithms. The developed algorithm will now be integrated in a web based system providing seamless access to browsing, retrieval, search and analysis of submitted scores.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8