Graphics Recognition. Recent Advances and Perspectives

1. Front Matter

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2. Platforms, Architectures and Document Knowledge Models

   1. Strategy for Line Drawing Understanding

      • Jean-Yves Ramel, Nicole Vincent

      Pages 1-12

   2. DocMining: A Cooperative Platform for Heterogeneous Document Interpretation According to User-Defined Scenarios

      • Eric Clavier, Gérald Masini, Mathieu Delalandre, Maurizio Rigamonti, Karl Tombre, Joël Gardes

      Pages 13-24

   3. Knowledge Representation and Acquisition for Engineering Document Analysis

      • Y. Saidali, S. Adam, J. M. Ogier, E. Trupin, J. Labiche

      Pages 25-37

   4. Dealing with Noise in DMOS, a Generic Method for Structured Document Recognition: An Example on a Complete Grammar

      • Bertrand Coüasnon

      Pages 38-49

3. Technical Documents, Maps and Charts

   1. Raster to Vector Conversion of Color Cartographic Maps

      • Serguei Levachkine

      Pages 50-62

   2. Text/Graphics Separation and Recognition in Raster-Scanned Color Cartographic Maps

      • Aurelio Velázquez, Serguei Levachkine

      Pages 63-74

   3. Resolving Ambiguities in Toponym Recognition in Cartographic Maps

      • Alexander Gelbukh, Serguei Levachkine, Sang-Yong Han

      Pages 75-86

   4. Model-Based Chart Image Recognition

      • Weihua Huang, Chew Lim Tan, Wee Kheng Leow

      Pages 87-99

   5. Extracting System-Level Understanding from Wiring Diagram Manuals

      • Larry Baum, John Boose, Molly Boose, Carey Chaplin, Ron Provine

      Pages 100-108

   6. Automatic Generation of Layered Illustrated Parts Drawings for Advanced Technical Data Systems

      • Molly L. Boose, David B. Shema, Lawrence S. Baum

      Pages 109-115

   7. Main Wall Recognition of Architectural Drawings Using Dimension Extension Line

      • Jaehwa Park, Young-Bin Kwon

      Pages 116-127

4. Perceptual Organization, Indexing and Graphical Signatures

   1. Interactive Recognition of Graphic Objects in Engineering Drawings

      • Yan Luo, Wenyin Liu

      Pages 128-141

   2. Skewed Mirror Symmetry for Depth Estimation in 3D Line-Drawings

      • Ana Piquer, Ralph Martin, Pedro Company

      Pages 142-153

   3. Vectorial Signatures for Symbol Discrimination

      • Philippe Dosch, Josep Lladós

      Pages 154-165

   4. Syntactic Models to Represent Perceptually Regular Repetitive Patterns in Graphic Documents

      • Gemma Sánchez, Josep Lladós

      Pages 166-175

   5. Indexing Technical Symbols Using Ridgelets Transform

      • Oriol Ramos Terrades, Ernest Valveny

      Pages 176-187

5. Image Analysis and Low-Level Processing

   1. Automatic Measuring the Local Thickness of Raster Lines

      • Alexander Gribov, Eugene Bodansky

      Pages 188-192

   2. Approximation of Polylines with Circular Arcs

      • Eugene Bodansky, Alexander Gribov

      Pages 193-198

   3. Adaptable Vectorisation System Based on Strategic Knowledge and XML Representation Use

      • Mathieu Delalandre, Youssouf Saidali, Eric Trupin, Jean-Marc Ogier

      Pages 199-210

1 Thisbookcontainsrefereedandimprovedpaperspresentedatthe5thIAPR - ternational Workshop on Graphics Recognition (GREC 2003). GREC 2003 was held in the Computer Vision Center, in Barcelona (Spain) during July 30–31, 2003. TheGRECworkshopisthemainactivityoftheIAPR-TC10,theTechnical 2 Committee on Graphics Recognition . Edited volumes from the previous wo- shops in the series are available as Lecture Notes in Computer Science: LNCS Volume 1072 (GREC 1995 at Penn State University, USA), LNCS Volume 1389 (GREC 1997 in Nancy, France), LNCS Volume 1941 (GREC 1999 in Jaipur, India), and LNCS Volume 2390 (GREC 2001 in Kingston, Canada). Graphics recognition is a particular ?eld in the domain of document ana- sis that combines pattern recognition and image processing techniques for the analysis of any kind of graphical information in documents, either from paper or electronic formats. Topics of interest for the graphics recognition community are: vectorization; symbol recognition; analysis of graphic documents with - agrammatic notation like electrical diagrams, architectural plans, engineering drawings, musical scores, maps, etc. ; graphics-based information retrieval; p- formance evaluation in graphics recognition; and systems for graphics recog- tion. Inadditiontotheclassicobjectives,inrecentyearsgraphicsrecognitionhas faced up to new and promising perspectives, some of them in conjunction with other, a?ne scienti?c communities. Examples of that are sketchy interfaces and on-line graphics recognition in the framework of human computer interaction, or query by graphic content for retrieval and browsing in large-format graphic d- uments, digital libraries and Web applications. Thus, the combination of classic challenges with new research interests gives the graphicsrecognition ?eld an active scienti?c community, with a promising future.

• Performance
• algorithms
• cartographic map analysis
• classification
• diagram analysis
• document analysis
• document image recognition
• engineering drawing analysis
• genetic algorithms
• graphic analysis
• image analysis
• knowledge representation
• object recognition
• symbol recognition
• visual storytelling
• algorithm analysis and problem complexity

• Computer Vision Center, Universitat Autònoma de Barcelona, Bellaterra, (Barcelona), Spain

  Josep Lladós

• Departmen Computer Science and Engineering, Chung-Ang University, Seoul, Korea

  Young-Bin Kwon

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