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


Publications

  • 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]