Signs and Universal Text

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Overview

Visually impaired individuals have achieved great autonomy using a combination of traditional aids and more recent advances suchs as GPS, reading devices for printed text, and other technologies. However, the desire or need to read street signs, store front banners, marquees, and other forms of text that are ubiquitous cannot yet be met without the aid of another person.


Our goal is to develop algorithms for robustly reading text in complex indoor and outdoor environments. We focus our efforts on three central issues:
  • Increased accuracy of detection and recognition.
  • Incorporating user input and goals.
  • Graceful failure to minimize harmful effects.

Our previous work is summarized on the prior VIDI Project web page.


Faculty


Graduate Students


Alumni


Collaborators


References

  • Jacqueline Feild, Erik Learned-Miller.
    Improving Open-Vocabulary Scene Text Recognition In Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2013.
  • Jacqueline Feild, Erik Learned-Miller, David A. Smith.
    Using a Probabilistic Syllable Model to Improve Scene Text Recognition In Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2013.
  • Yahan Zhou, Jacqueline Feild, Rui Wang, Erik Learned-Miller
    Scene Text Segmentation via Inverse Rendering In Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2013.
  • David L. Smith, Jacqueline Feild, and Erik Learned-Miller.
    Enforcing Similarity Constraints with Integer Programming for Better Scene Text Recognition
    IEEE Computer Vision and Pattern Recognition (CVPR), June 2011.
  • Jerod J. Weinman, Erik Learned-Miller, and Allen Hanson.
    Scene Text Recognition using Similarity and a Lexicon with Sparse Belief Propagation
    IEEE Transations on Pattern Analysis and Machine Intelligence (PAMI) 21, Oct. 2009.
  • Jerod J. Weinman, Erik Learned-Miller, and Allen Hanson.
    A Discriminative Semi-Markov Model for Robust Scene Text Recognition
    Intl. Conference on Pattern Recognition (ICPR), Dec, 2008.
  • Jerod J. Weinman, Allen Hanson, and Erik Learned-Miller.
    Fast Lexicon-Based Scene Text Recognition with Sparse Belief Propagation
    Intl. Conference on Document Analysis and Recognition (ICDAR), Sept. 2007.
  • Jerod J. Weinman and Erik Learned-Miller.
    Improving Recognition of Novel Input with Similarity
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2006.