Multimedia Tools and Applications

, Volume 75, Issue 11, pp 6463–6479 | Cite as

Musicologist-driven writer identification in early music manuscripts

  • Masahiro Niitsuma
  • Lambert Schomaker
  • Jean-Paul van Oosten
  • Yo Tomita
  • David Bell


Recent renewed interest in computational writer identification has resulted in an increased number of publications. In relation to historical musicology its application has so far been limited. One of the obstacles seems to be that the clarity of the images from the scans available for computational analysis is often not sufficient. In this paper, the use of the Hinge feature is proposed to avoid segmentation and staff-line removal for effective feature extraction from low quality scans. The use of an auto encoder in Hinge feature space is suggested as an alternative to staff-line removal by image processing, and their performance is compared. The result of the experiment shows an accuracy of 87 % for the dataset containing 84 writers’ samples, and superiority of our segmentation and staff-line removal free approach. Practical analysis on Bach’s autograph manuscript of the Well-Tempered Clavier II (Additional MS. 35021 in the British Library, London) is also presented and the extensive applicability of our approach is demonstrated.


Image processing Writer identification Domain driven data mining Optical music recognition 


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Masahiro Niitsuma
    • 4
  • Lambert Schomaker
    • 2
  • Jean-Paul van Oosten
    • 2
  • Yo Tomita
    • 1
  • David Bell
    • 3
  1. 1.School of Creative ArtsQueen’s University of BelfastBelfastUK
  2. 2.Department of Artificial IntelligenceUniversity of GroningenGroningenNetherlands
  3. 3.School of Electronics, Electrical Engineering and Computer ScienceQueen’s University BelfastBelfastUK
  4. 4.Department of Media Technology, College of Information Science and EngineeringRitsumeikan UniversityShigaJapan

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