i-Street: Detection, Identification, Augmentation of Street Plates in a Touristic Mobile Application

  • Stefano Messelodi
  • Carla Maria ModenaEmail author
  • Lorenzo Porzi
  • Paul Chippendale
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9280)


Smartphone technology with embedded cameras, sensors, and powerful computational resources have made mobile Augmented Reality possible. In this paper, we present i-Street, an Android touristic application whose aim is to detect, identify and read the street plates in a video flow and then to estimate relative pose in order to accurately augment them with virtual overlays. The system was successfully tested in the historical centre of Grenoble (France), proving to be robust to outdoor illumination conditions and to device pose variance. The average identification rate in realistic laboratory tests was about 82%, remaining cases were rejected with no false positives.


Augmented reality Mobile devices Text in scene images 


  1. 1.
    Huang, Z., Hui, P., Peylo, C., Chatzopoulos, D.: Mobile augmented reality survey: a bottom-up approach. Technical Report arXiv:1309.4413, HKUST, Hong Kong University of Science and Technology (2013)
  2. 2.
  3. 3.
    Schipperijn, J., Kerr, J., Duncan, S., Madsen, T., Demant Klinker, C., Troelsen, J.: Dynamic Accuracy of GPS Receivers for Use in Health Research: A Novel Method to Assess GPS Accuracy in Real-World Settings. Frontiers in Public Health 2(21) (2014)Google Scholar
  4. 4.
    VENTURI - ImmersiVe ENhancemenT of User-woRld Interactions: EC FP7-ICTProject. (2011–2014)
  5. 5.
    Jung, K., Kim, K.I., Jain, A.K.: Text Information Extraction in Images and Video: a Survey. Pattern Recognition 37(5), 977–997 (2004)CrossRefGoogle Scholar
  6. 6.
    Ye, Q., Doermann, D.: Text Detection and Recognition in Imagery: A Survey. IEEE Transaction on Pattern Analysis and Machine Intelligence (2015)Google Scholar
  7. 7.
    Messelodi, S., Modena, C.M.: Automatic Identification and Skew Estimation of Text Lines in Real Scene Images. Pattern Recognition 32, 791–810 (1999)CrossRefGoogle Scholar
  8. 8.
    Messelodi, S., Modena, C.M.: Scene Text Recognition and Tracking to Identify Athletes in Sport Videos. Multimedia Tools and Applications, Special Issue on Automated Information Extraction in Media Production 63(2), 521–545 (2013)Google Scholar
  9. 9.
    Smith, R.: An Overview of the Tesseract OCR Engine. In 9th International Conference on Document Analysis and Recognition, Curitiba, Brazil, pp. 629–633 (2007)Google Scholar
  10. 10.
    Lee, D-S., Smith, R.: Improving Book OCR by Adaptive Language and Image Models. In: 10th IAPR International Workshop on Document Analysis Systems, pp. 115–119 (2012)Google Scholar
  11. 11.
    Myers, E.: An O(ND) Difference Algorithm and its Variations. Algorithmica 1(2), 251–266 (1986)CrossRefMathSciNetzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Stefano Messelodi
    • 1
  • Carla Maria Modena
    • 1
    Email author
  • Lorenzo Porzi
    • 1
    • 2
  • Paul Chippendale
    • 1
  1. 1.FBK-irstPovo, TrentoItaly
  2. 2.University of PerugiaPerugiaItaly

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