• International Journal of Contents
• /
• v.12 no.4
• /
• pp.53-61
• /
• 2016

Although extensively developed, optical music recognition systems have mostly focused on musical symbols (notes, rests, etc.), while disregarding the chord symbols. The process becomes difficult when the images are distorted or slurred, although this can be resolved using optical character recognition systems. Moreover, the appearance of outliers (lyrics, dynamics, etc.) increases the complexity of the chord recognition. Therefore, we propose a new approach addressing these issues. After binarization, un-distortion, and stave and lyric removal of a musical image, a rule-based method is applied to detect the potential regions of chord symbols. Next, a lexicon-driven approach is used to optimally and simultaneously separate and recognize characters. The score that is returned from the recognition process is used to detect the outliers. The effectiveness of our system is demonstrated through impressive accuracy of experimental results on two datasets having a variety of resolutions.

• Proceedings of the Korea Contents Association Conference
• /
• 2015.05a
• /
• pp.53-54
• /
• 2015

Optical music recognition (OMR) systems have been developed in recent years. However, chord symbols that play a role in a music sheet have been still disregarded. Therefore, we aimed to develop a proper approach to recognize these chord symbols. First, we divide the image of chord symbol into small segments in horizontal by a method based on vertical projection. Then, the optimal combination of these segments is found by using a lexicon-driven word scoring technique and a nearest neighbor classifier. The word that corresponds to the optimal combination is the result of recognition. The experiment gives an impressive result with accuracy 97.32%.