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Text Localization Based on Fast Feature Pyramids and Multi-Resolution Maximally Stable Extremal Regions

  • Alessandro Zamberletti
  • Lucia Noce
  • Ignazio Gallo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9009)

Abstract

Text localization from scene images is a challenging task that finds application in many areas. In this work, we propose a novel hybrid text localization approach that exploits Multi-resolution Maximally Stable Extremal Regions to discard false-positive detections from the text confidence maps generated by a Fast Feature Pyramid based sliding window classifier. The use of a multi-scale approach during both feature computation and connected component extraction allows our method to identify uncommon text elements that are usually not detected by competing algorithms, while the adoption of approximated features and appropriately filtered connected components assures a low overall computational complexity of the proposed system.

Keywords

Text Component Scene Image Text Localization Text Detection Image Pyramid 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alessandro Zamberletti
    • 1
  • Lucia Noce
    • 1
  • Ignazio Gallo
    • 1
  1. 1.Department of Theoretical and Applied ScienceUniversity of InsubriaVareseItaly

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