Automatic Pill Identification from Pillbox Images

David E. Madsen, Katie S. Payne, Jason Hagerty, Nathan Szanto, Mark Wronkiewicz, Randy H. Moss, William V. Stoecker


There is a vital need for fast and accurate recognition of medicinal tablets and capsules. Efforts to date have centered on automatic segmentation, color and shape identification. Our system combines these with pre-processing before imprint recognition. Using the National Library of Medicine Pillbox database, regression analysis applied to automatic color and shape recognition allows for successful pill identification. Measured errors for the subtasks of segmentation and color recognition for this database are 1.9% and 2.2%, respectively. Imprint recognition with optical character recognition (OCR) is key to exact pill ID, but remains a challenging problem, therefore overall recognition accuracy is not yet known.


  1. Apostolico, A., & Galil, Z. (1997). Pattern matching algorithms. Oxford: Oxford University Press, p. 123- 125.
  2. Arthur, D., & Vassilvitskii, S. (2007). K-means++: The Advantages of Careful Seeding. In Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, 1027-1035.
  3. Gonzalez, R. C., & Woods, R. E. (2008). Digital Image Processing (3rd ed.). New Jersey: Pearson Education.
  4. Hartl, A. (2010). Computer-Vision Based Pharmaceutical Pill Recognition on Mobile Phones. CESCG 14th Central European Seminar on Computer Graphics.
  5. Hu, M.-K. (1962). Visual pattern recognition by moment invariants. IRE Transactions on Information Theory, 8(2), p. 179-87.
  6. Itseez. (2012). OpenCV. Open Source Computer Vision Library.
  7. Lee, Y., Park, U., Jain, A. K., & Lee, S. (2012). Pill-ID: Matching and retrieval of drug pill images. Pattern Recognition Letters, 33(7), p.904-910.
  8. Menard, S. (2001). Applied Logistic Regression (2nd ed.). Thousand Oaks: Sage Publications, Inc.
  9. Moore, T. J., Cohen, M. R., & Furberg, C. D. (2007). Serious adverse drug events reported to the Food and Drug Administration, 1998-2005. Archives of Internal Medicine, 167(16), 1752-9.
  10. Smith, R. (2012). Tesseract Code. p/tesseract-ocr
  11. Szeliski, R. (2011). Computer Vision: Algorithms and Applications. New York: Springer.
  12. Umbaugh, S. E. (2011). Digital Image Processing and Analysis (2nd ed.). Boca Raton: CRC Press.
  13. United States National Library of Medicine. (2012). Pill Beta. National Institutes of Health.
  14. Xu, R., & Wunsch, D. (2005). Survey of clustering

Paper Citation

in Harvard Style

E. Madsen D., S. Payne K., Hagerty J., Szanto N., Wronkiewicz M., H. Moss R. and V. Stoecker W. (2013). Automatic Pill Identification from Pillbox Images . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 378-384. DOI: 10.5220/0004303603780384

in Bibtex Style

author={David E. Madsen and Katie S. Payne and Jason Hagerty and Nathan Szanto and Mark Wronkiewicz and Randy H. Moss and William V. Stoecker},
title={Automatic Pill Identification from Pillbox Images},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},

in EndNote Style

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)
TI - Automatic Pill Identification from Pillbox Images
SN - 978-989-8565-47-1
AU - E. Madsen D.
AU - S. Payne K.
AU - Hagerty J.
AU - Szanto N.
AU - Wronkiewicz M.
AU - H. Moss R.
AU - V. Stoecker W.
PY - 2013
SP - 378
EP - 384
DO - 10.5220/0004303603780384