Learning of structural descriptions of graphic symbols using deformable template matching

Ernest Valveny, Enric Marti

    Research output: Contribution to journalArticleResearchpeer-review

    8 Citations (Scopus)

    Abstract

    Accurate symbol recognition in graphic documents needs an accurate representation of the symbols to be recognized. If structural approaches are used for recognition, symbols have to be described in terms of their shape, using structural relationships among extracted features. Unlike statistical pattern recognition, in structutal methods, symbols are usually manually defined from expertise knowledge, and not automatically inferedfrom sample images. In this work we explain one approach to learn from examples a representative structural description of a symbol, thus providing better information about shape variability. The description of a symbol is based on a probabilistic model. It consists of a set of lines described by the mean and the variance of line parameters, respectively providing information about the model of the symbol, and its shape variability. The representation of each image in the sample set as a set of lines is achieved using deformable template matching. © 2001 IEEE.
    Original languageEnglish
    Article number953831
    Pages (from-to)455-459
    JournalProceedings of the International Conference on Document Analysis and Recognition, ICDAR
    Volume2001-January
    DOIs
    Publication statusPublished - 1 Jan 2001

    Fingerprint Dive into the research topics of 'Learning of structural descriptions of graphic symbols using deformable template matching'. Together they form a unique fingerprint.

    Cite this