• DocumentCode
    3488094
  • Title

    Constructing a Hierarchical Structure from Symbol Alphabets of Technical Line Drawings

  • Author

    Nayef, N. ; Breuel, Thomas M.

  • Author_Institution
    Tech. Univ. Kaiserslautern, Kaiserslautern, Germany
  • fYear
    2013
  • fDate
    25-28 Aug. 2013
  • Firstpage
    773
  • Lastpage
    777
  • Abstract
    This paper presents a method for analysing symbol alphabets of technical line drawings and finding their underlying structure, which is important for investigating (dis)similarity of different symbols. The proposed method constructs a hierarchical structure of a set of technical symbols. The method is based on agglomerative hierarchical clustering that uses either of two variants as a similarity measure: either geometric matching between symbols´ shapes, or an off-the-shelf shape descriptor. Identifying such a hierarchical structure of a set of symbols can improve symbol recognition / spotting systems, as it helps with scalability issues, and provides information on the degree of similarity among symbols, so that those systems can automatically adapt their parameter values for more accurate recognition. Our method has been tested on the symbol alphabet of the symbol recognition / spotting contest of GREC-2011, and achieved promising results.
  • Keywords
    image matching; image retrieval; pattern clustering; agglomerative hierarchical clustering; geometric matching; hierarchical technical symbol structure; scalability; symbol alphabet analysis; symbol dissimilarity; symbol recognition spotting contest; symbol recognition systems; symbol shape descriptor; symbol similarity measure; symbol spotting systems; technical line drawings; Accuracy; Databases; Shape; Text analysis; Vectors; Visualization; hierarchical shape clustering; structure of symbol alphabets; technical line drawings;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Document Analysis and Recognition (ICDAR), 2013 12th International Conference on
  • Conference_Location
    Washington, DC
  • ISSN
    1520-5363
  • Type

    conf

  • DOI
    10.1109/ICDAR.2013.158
  • Filename
    6628723