Effective staff line detection, restoration and removal approach for different quality of scanned handwritten music sheets

  • Authors

    • Fatemeh Alirezazadeh Dept. of Electrical and Computer Engineering, Najaf Abad University, Isfahan, Iran
    • Mohammad Reza Ahmadzadeh Dept. of Electrical and Computer Engineering, Isfahan University of Technology (IUT), Iran
    2014-08-24
    https://doi.org/10.14419/jacst.v3i2.3196

  • Musical staff detection and removal is one of the most important preprocessing steps of an Optical Music Recognition (OMR) system. This paper proposes a new method for detecting and restoring staff lines from global information of music sheets. First of all the location of staff lines is determined. Therefore, music staff is sliced. The staff line segments are recognized at each slice and then with adequate knowledge of staff line locations, the deformed, interrupted or partly removed staff lines can be rebuilt. A new approach for staff removal algorithm is suggested in this paper fundamentally based on removing all detected staff lines. At last, the Fourier transform and Gaussian lowpass filter will help to reconstruct the separated and interrupted symbols. It has been tested on the dataset of the musical staff removal competition held under ICDAR 2012. The experimental results show the effectiveness of this method under various kinds of deformations in staff lines.

    Keywords: Fourier Transform, Gaussian Low Pass Filter, Optical Music Recognition, Run Length Coding, Staff Line Removal.

  • References

    1. A. Fornes, A. Dutta, A. Gordo and J. Llados, "The ICDAR 2012 music scores competition: Staff removal and writer identification," ICDAR, 2013.
    2. I. Fujinaga, "Staff detection and removal," In: George S (ed) Visual perception of music notation, on-line and off-line recognition, 2004. http://dx.doi.org/10.4018/978-1-59140-298-5.ch001.
    3. A. Rebelo and J. S. Cardoso, "Staff Line Detection and Removal in the Grayscale Domain," 12th International Conference on Document Analysis and Recognition ICDAR, 2013, pp. 57-61.
    4. D. Bainbridge and T. Bell, "A music notation construction engine for optical music recognition," Softw Pract Exp vol. 33, no. 2, pp. 173–200, 2003. http://dx.doi.org/10.1002/spe.502.
    5. C. Genfang, Z. Liyin, Z. Wenjun and W. Qiuqiu, "Detecting the staff-lines of musical score with hough transform and mathematical morphology," International Conference on Multimedia Technology (ICMT), 2010, pp. 1-4.
    6. J.S. Cardoso, A. Capela, A. Rebelo, C. Guedes and J.P. da Costa, "Staff detection with stable paths," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 31, no. 6, pp. 1134-1139, 2009. http://dx.doi.org/10.1109/TPAMI.2009.34.
    7. A. Rebelo, F. Paszkiewics and C. Guedes, "A method for music symbols extraction based on musical rules," Bridges: Mathematics, Music, Art, Architecture, Culture, pp. 81-87, 2011.
    8. A. Fornes, A. Dutta, A. Gordo, and J. Llados, "CVC-MUSCIMA: a ground truth of handwritten music score images for writer identification and staff removal," International Journal on Document Analysis and Recognition, vol. 15, 2012, pp. 243–251. http://dx.doi.org/10.1007/s10032-011-0168-2.
    9. C. Dalitz, M. Droettboom, B. Pranzas, and I. Fujinaga, "A comparative study of staff removal algorithms," IEEE Transactions on PAMI, vol. 30, no. 5, pp. 753–766, May 2008. http://dx.doi.org/10.1109/TPAMI.2007.70749.
    10. T. Pinto, A. Rebelo, G. Giraldi and J.S. Cardoso, "Music score binarization based on domain knowledge," pattern recognition and image analysis. Lecture notes in computer science, vol. 6669. Springer, Heidelberg, pp. 700–708, 2011. http://dx.doi.org/10.1007/978-3-642-21257-4_87.
    11. B. Su, S. Lu, U. Pal and C. L. Tan, "An effective staff detection and removal technique for musical documents," 10th International Workshop on Document Analysis Systems, Queensland, Australia, March 2012, pp. 160-164.
    12. R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed., Prentice Hall, Upper Saddle River, NJ, 2008.

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  • How to Cite

    Alirezazadeh, F., & Ahmadzadeh, M. R. (2014). Effective staff line detection, restoration and removal approach for different quality of scanned handwritten music sheets. Journal of Advanced Computer Science & Technology, 3(2), 136-142. https://doi.org/10.14419/jacst.v3i2.3196