Text Processing

Overview

Text is everywhere in our world, from the documents we read to the road signs and movie marquees that help us navigate our environments. At UMass, we are investigating both the traditional problem of machine-printed document recognition, commonly referred to as Optical Character Recognition, or OCR, and the more difficult computer vision problem of universal text recognition which concerns recognizing text wherever it might appear, such as on store front signs.


To learn more about each research effort, follow these links:


Faculty


Graduate Students


Alumni


Publications

  • Jacqueline Feild, Erik Learned-Miller.
    Improving Open-Vocabulary Scene Text Recognition In Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2013. [pdf]
  • 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. [pdf]
  • 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. [pdf]
  • Gary B. Huang, Andrew Kae, Carl Doersch, Erik Learned-Miller
    Bounding the Probability of Error for High Precision Optical Character Recognition
    Journal of Machine Learning Research (JMLR), 2012.
    [pdf].
  • David L. Smith, Jacqueline Feild, Erik Learned-Miller.
    Enforcing Similarity Constraints with Integer Programming for Better Scene Text Recognition
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
    [pdf]
  • Andrew Kae, Kin Kan, Vijay K Narayanan, Dragomir Yankov
    Categorization of Display Ads using Image and Landing Page Features
    The Third Workshop on Large-scale Data Mining: Theory and Applications'11 (LDMTA'11), in conjunction with SIGKDD2011, to appear.
    [pdf]
  • Andrew Kae, David A. Smith, and Erik Learned-Miller
    Learning on the Fly: A font-free approach towards multilingual OCR
    International Journal on Document Analysis and Recognition (IJDAR)
    [pdf] [Springer]
  • Andrew Kae, Gary Huang, Carl Doersch, and Erik Learned-Miller
    Improving State-of-the-Art OCR through High-Precision Document-Specific Modeling
    Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010.
    [pdf]
  • Andrew Kae, Gary Huang, and Erik Learned-Miller
    Bounding the Probability of Error for High Precision Recognition.
    Technical Report UM-CS-2009-031, Dept. of Computer Science, University of Massachusetts, Amherst, 2009.
    [pdf] [arxiv.org]
  • Andrew Kae and Erik Learned-Miller. Learning on the fly: Font free approaches to difficult OCR problems.
    Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2009.
    [pdf]
  • Jerod Weinman, Erik Learned Miller, and Allen Hanson.
    Scene text recognition using similarity and a lexicon with sparse belief propagation.
    To appear in IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Special Issue on Probabilistic Graphical Models, 2009.
    [pdf]
  • Jerod Weinman, Erik Learned Miller, and Allen Hanson.
    A discriminative semi-Markov model for robust scene text recognition.
    International Conference on Pattern Recognition (ICPR),2008.
    [pdf]
  • Michael Wick, Michael G. Ross and Erik Learned-Miller.
    Context-Sensitive Error Correction: Using Topic Models to Improve OCR.
    Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2007.
    [pdf]
  • Jerod Weinman, Erik Learned-Miller, and Allen Hanson.
    Fast Lexicon-Based Scene Text Recognition with Sparse Belief Propagation.
    Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2007.
    [pdf]
  • Gary C. Huang, Erik Learned-Miller, and Andrew McCallum.
    Cryptogram Decoding for OCR using Numerization Strings.
    Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2007.
    [pdf]
  • Jerod Weinman and Erik Learned-Miller.
    Improving recognition of novel input with similarity.
    In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Volume 1, pp. 308-315, 2006.
    [pdf]
  • Jerod J. Weinman, Allen Hanson and Erik Learned-Miller.
    Joint feature selection for object detection and recognition.
    UMass Amherst Technical Report 06-54, 8 pages, 2006.
    [pdf]
  • Gary Huang, Erik Learned-Miller and Andrew McCallum.
    Cryptogram decoding for optical character recognition.
    UMass Amherst Technical Report 06-45, 12 pages, 2006.
    [pdf]
  • Erik Miller and Paul Viola.
    Ambiguity and constraint in mathematical expression recognition.
    Proceedings of the National Conference of Artificial Intelligence (AAAI), pp. 784-791, 1998.
    [pdf]