z-logo
open-access-imgOpen Access
A Shortest Path Approach for Staff Line Detection
Author(s) -
Ana Rebelo,
Artur Capela,
Joaquim Pinto da Costa,
Carlos Guedes,
Eurico Carrapatoso,
Jaime S. Cardoso
Publication year - 2007
Publication title -
portuguese national funding agency for science, research and technology (rcaap project by fct)
Language(s) - English
Resource type - Conference proceedings
ISBN - 0-7695-3030-3
DOI - 10.1109/axmedis.2007.2
Subject(s) - computer science , line (geometry) , process (computing) , shortest path problem , path (computing) , task (project management) , curvature , reading (process) , artificial intelligence , carry (investment) , dijkstra's algorithm , speech recognition , computer vision , pattern recognition (psychology) , theoretical computer science , graph , mathematics , programming language , engineering , geometry , systems engineering , finance , law , political science , economics
Many music works produced in the past still exist only as original manuscripts or as photocopies. Preserving them entails their digitalization and consequent accessibility in a digital format easy-to-manage. The manual process to carry out this task is very time consuming and error prone. Optical music recognition (OMR) is a form of structured document image analysis where music symbols are isolated and identified so that the music can be conveniently processed. While OMR systems perform well on printed scores, current methods for reading handwritten musical scores by computers remain far from ideal. One of the fundamental stages of this process is the staff line detection. In this paper a new method for the automatic detection of music stave lines based on a shortest path approach is pre- sented. Lines with some curvature, discontinuities, and inclination are robustly detected. The proposed algorithm behaves favourably when compared experimentally with well-established algorithms.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom