ICDAR 2013 Music Scores Competition: Staff Removal

Abstract

The first competition on music scores that was organized at ICDAR in 2011 awoke the interest of researchers, who participated both at staff removal and writer identification tasks. In this second edition, we focus on the staff removal task and simulate a real case scenario: old music scores. For this purpose, we have generated a new set of images using two kinds of degradations: local noise and 3D distortions. This paper describes the dataset, distortion methods, evaluation metrics, the participant's methods and the obtained results.

Music staff removal with supervised pixel classification

“…These sets comprise different deformations applied over original scores: 3D distortions in TS1, local noise in TS2, and both 3D distortion and local noise in TS3. For a detailed description about each Best values, on average, achieved on each subset are highlighted participant and the deformation models applied, reader is referred to the report of the competition [34]. Our best average configuration (SVM with 81 features) is also included for comparison.…”

“…The current performance of staff removal methods can be checked in the GREC/ICDAR 2013 staff removal competition [12,34]. This competition makes use of the CVC-MUSCIMA database [11], which contains handwritten music score images with a perfect ground truth on staff removal.…”

DocCreator: A New Software for Creating Synthetic Ground-Truthed Document Images

“…The DocCreator ability to create synthetic documents that mimic real ones is effective for typewritten and handwritten characters (as long as the characters are apart from one another). Images created with DocCreator have already been used in many DIAR contexts: text/background/image pixel classification [36]; staff removal [13,37,38]; and handwritten character recognition [39]. In this article we present how DocCreator can be useful to enhance a binarization algorithm and for OCR performance prediction.…”

“…As a result, the extended database contains 6000 semi-synthetic grayscale images and 6000 semi-synthetic binary images. This database has been used in the second edition of the music score competition ICDAR 2013 [37]. Five participants submitted eight methods.…”

“…Regarding the results on the 3D distortion set, the submitted methods seem less robust to global distortion than to the presence of small curves and folds. For more details about the participants, the methods and the contest protocol, refer to [37]. This database has already become a benchmark database for musical document images analysis and recognition, as stated in [53].…”