A Symbol Spotting Approach Based on the Vector Model and a Visual Vocabulary

This paper addresses the difficult problem of symbol spotting for graphic documents. We propose an approach where each graphic document is indexed as a text document by using the vector model and an inverted file structure. The method relies on a visual vocabulary built from a shape descriptor adapted to the document level and invariant under classical geometric transforms (rotation, scaling and translation). Regions of interest selected with high degree of confidence using a voting strategy are considered as occurrences of a query symbol. Experimental results are promising and show the feasibility of our approach.

[1]  Josep Lladós,et al.  Symbol Spotting in Technical Drawings Using Vectorial Signatures , 2005, GREC.

[2]  Joaquim A. Jorge,et al.  Content-based retrieval of technical drawings , 2005, Int. J. Comput. Appl. Technol..

[3]  Salvatore Tabbone,et al.  An Indexing Method for Graphical Documents , 2007, Ninth International Conference on Document Analysis and Recognition (ICDAR 2007).

[4]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[5]  Dan Roth,et al.  Learning to detect objects in images via a sparse, part-based representation , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Tony P. Pridmore,et al.  Building Synthetic Graphical Documents for Performance Evaluation , 2007, GREC.

[7]  Andrew Zisserman,et al.  Video Google: Efficient Visual Search of Videos , 2006, Toward Category-Level Object Recognition.

[8]  Salvatore Tabbone,et al.  Symbol Descriptor Based on Shape Context and Vector Model of Information Retrieval , 2008, 2008 The Eighth IAPR International Workshop on Document Analysis Systems.

[9]  Wenyin Liu,et al.  An interactive example-driven approach to graphics recognition in engineering drawings , 2006, International Journal of Document Analysis and Recognition (IJDAR).

[10]  Frédéric Jurie,et al.  Creating efficient codebooks for visual recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[11]  Josep Lladós,et al.  A Region-Based Hashing Approach for Symbol Spotting in Technical Documents , 2007, GREC.