Facilitation of air traffic control via optical character recognition-based aircraft registration number extraction

Facilitation of air traffic control via optical character recognition-based aircraft registration number extraction

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To identify any aircraft in the world, it is sufficient to read its registration number. This number is a unique identifier, and offers valuable information, in the same way a car registration number does. In this work, the authors present the results of their feasibility study towards a simple, yet very efficient and effective system to identify aircrafts using video-optical character recognition acquired by off-the-shelf cameras. They used several videos under realistic conditions at the Heraklion airport during high season and they achieved very promising results. They claim that there is much room for the development of a low-cost airport surface monitoring system based on standard cameras, which can complement high-cost radars.


    1. 1)
      • 1. ICAO. ‘International Civil Aviation Organization’, 2015. Available at
    2. 2)
      • 2. Berlanga, A., Besada, J.A., Herrero, J.G., et al: ‘Aircraft identification integrated into an airport surface surveillance video system’, Mach. Vis. Appl., 2004, 15, (3), pp. 164171.
    3. 3)
      • 3. Ali, S., Choudhry, M.: ‘A generalized higher order neural network for aircraft recognition in a video docking system’, Neural Comput. Appl., 2010, 19, (1), pp. 1921.
    4. 4)
      • 4. Berlanga, A., Garcia-Herrero, J., Molina, J., et al: ‘OCR parameters tuning by means of evolution strategies for aircraft's tail number recognition’. Evolutionary Computation, Honolulu, 2002.
    5. 5)
      • 5. Besada, J., Garcia, J., Portillo, M.J., et al: ‘Airport surface surveillance based on video images’, IEEE Trans. Aerosp. Electron. Syst., 2005, 41, (3), pp. 10751082.
    6. 6)
      • 6. Dudani, S., Breeding, K., McGhee, R.: ‘Aircraft identification by moment invariants’, IEEE Trans. Comput., 1977, C-26, (1), pp. 3946.
    7. 7)
      • 7. Pavlidou, N., Grammalidis, N., Dimitropoulos, K., et al: ‘IST INTERVUSE project: intergrated radar, flight plan and digital video data fusion for A-SMGCS’. ITS in Europe Congress, Budapest, 2004.
    8. 8)
      • 8. Jeremy, S.: ‘Application of an image feature network-based object recognition algorithm to aircraft detection and classification’. Automatic Target Recognition XXIV, Baltimore, MD, USA, 2014.
    9. 9)
      • 9. Eurocontrol: ‘Advanced Surface Movement Guidance and Control System (A-SMGCS)’, 2013.  Available at
    10. 10)
      • 10. Eurocontrol: ‘Surface Movement Radar’, ACDM..
    11. 11)
      • 11. Eurocontrol: ‘Automatic Dependent Surveillance Broadcast (ADS-B)’.
    12. 12)
      • 12. INTERVUSE’. Intergrated Radar, Flight Plan and Digital Video Data Fusion for SMGCS, 2012. Available at
    13. 13)
      • 13. Thirde, D., Borg, M., Ferryman, J.: ‘A real-time scene understanding system for airport apron Monitoring’ (ORION Group, INRIA Sophia-Antipolis, France, 2004).
    14. 14)
      • 14. Hudson, S., Psaltis, D.: ‘Correlation filters for aircraft identification from radar range profiles’, IEEE Trans. Aerospace Electron. Syst., 1993, 29, (3), pp. 741748.
    15. 15)
      • 15. Saghafi, F., Khansari Zadeh, S.M., Etminan Bakhsh, V.: ‘Aircraft visual identification by neural networks’, J. Aerospace Sci. Technol., 2008, 5, (3), pp. 123128.
    16. 16)
      • 16. Smith, R.: ‘An overview of the Tesseract OCR engine’. Int. Conf. Document Analysis and Recognition, Curitiba, Brazil, 2007.
    17. 17)
      • 17. Shafait, F., Keysers, D., Thomas, B.B.: ‘Efficient implementation of local adaptive thresholding techniques using integral images’, 2008.
    18. 18)
      • 18. Helinski, M., Kmieciak, M., Parkola, T.: ‘Report on the comparison of Tesseract and ABBYY FineReader OCR engines’, IMPACT technical report, 2012.
    19. 19)
      • 19. Rivera, A.: ‘Best document scanning services’. Available at

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