Symbol Spotting in Digital Libraries [electronic resource] : Focused Retrieval over Graphic-rich Document Collections /
Rusiñol, Marçal.author.
Lladós, Josep.author.
The specific problem of symbol recognition in graphical documents requires additional techniques to those developed for character recognition. The most well-known obstacle is the so-called Sayre paradox: Correct recognition requires good segmentation, yet improvement in segmentation is achieved using information provided by the recognition process. This dilemma can be avoided by techniques that identify sets of regions containing useful information. Such symbol-spotting methods allow the detection of symbols in maps or technical drawings without having to fully segment or fully recognize the entire content. This unique text/reference provides a complete, integrated and large-scale solution to the challenge of designing a robust symbol-spotting method for collections of graphic-rich documents. The book examines a number of features and descriptors, from basic photometric descriptors commonly used in computer vision techniques to those specific to graphical shapes, presenting a methodology which can be used in a wide variety of applications. Additionally, readers are supplied with an insight into the problem of performance evaluation of spotting methods. Some very basic knowledge of pattern recognition, document image analysis and graphics recognition is assumed. Topics and features: Includes a Foreword by Professor Karl Tombre, Director of the INRIA Nancy - Grand Est research center and Editor-in-Chief of Springer's International Journal on Document Analysis and Recognition Introduces the problems of symbol spotting and focused retrieval, outlining an architecture for solving these problems Examines techniques in computer vision for recognizing objects in scenes, and their application to the specific problem of spotting graphical symbols in documents Describes a method to determine probable locations of symbols in technical drawings which makes use of vectorial signatures as symbol descriptors Presents a spotting method which uses a prototype-based search as the basis for the focused retrieval task Explores an indexing method that retrieves locations of interest where a query symbol is likely to be found Investigates techniques for evaluating the performance of symbol-spotting systems, defined in terms of recognition abilities, location accuracy and scalability Researchers and practitioners in the field of graphics recognition, interested in the problem of symbol spotting and focused retrieval applications in the context of digital libraries, will find this an invaluable text on the subject. Dr. Marçal Rusiñol is a Research Associate at the Computer Vision Center and at the Computer Sciences Department of the Universitat Autònoma de Barcelona, Spain. Dr. Josep Lladós is Director of the Computer Vision Center and Associate Professor at the Computer Sciences Department of the Universitat Autònoma de Barcelona, Spain.
Description based upon print version of record.
Includes bibliographical references and index.
State-of-the-Art in Symbol Spotting -- On the Use of Photometric Descriptors for Symbol Spotting -- Symbol Spotting for Document Categorization -- On the Use of Geometric and Structural Constraints for Symbol Spotting -- Vectorial Signatures for Symbol Recognition and Spotting -- Symbol Spotting Through Prototype-based Search -- A Relational Indexing Method for Symbol Spotting -- A Performance Evaluation Protocol for Symbol Spotting Systems -- Performance Evaluation of Symbol Spotting Systems -- Conclusions.
The specific problem of symbol recognition in graphical documents requires additional techniques to those developed for character recognition. The most well-known obstacle is the so-called Sayre paradox: Correct recognition requires good segmentation, yet improvement in segmentation is achieved using information provided by the recognition process. This dilemma can be avoided by techniques that identify sets of regions containing useful information. Such symbol-spotting methods allow the detection of symbols in maps or technical drawings without having to fully segment or fully recognize the entire content. This unique text/reference provides a complete, integrated and large-scale solution to the challenge of designing a robust symbol-spotting method for collections of graphic-rich documents. The book examines a number of features and descriptors, from basic photometric descriptors commonly used in computer vision techniques to those specific to graphical shapes, presenting a methodology which can be used in a wide variety of applications. Additionally, readers are supplied with an insight into the problem of performance evaluation of spotting methods. Some very basic knowledge of pattern recognition, document image analysis and graphics recognition is assumed. Topics and features: Includes a Foreword by Professor Karl Tombre, Director of the INRIA Nancy - Grand Est research center and Editor-in-Chief of Springer's International Journal on Document Analysis and Recognition Introduces the problems of symbol spotting and focused retrieval, outlining an architecture for solving these problems Examines techniques in computer vision for recognizing objects in scenes, and their application to the specific problem of spotting graphical symbols in documents Describes a method to determine probable locations of symbols in technical drawings which makes use of vectorial signatures as symbol descriptors Presents a spotting method which uses a prototype-based search as the basis for the focused retrieval task Explores an indexing method that retrieves locations of interest where a query symbol is likely to be found Investigates techniques for evaluating the performance of symbol-spotting systems, defined in terms of recognition abilities, location accuracy and scalability Researchers and practitioners in the field of graphics recognition, interested in the problem of symbol spotting and focused retrieval applications in the context of digital libraries, will find this an invaluable text on the subject. Dr. Marçal Rusiñol is a Research Associate at the Computer Vision Center and at the Computer Sciences Department of the Universitat Autònoma de Barcelona, Spain. Dr. Josep Lladós is Director of the Computer Vision Center and Associate Professor at the Computer Sciences Department of the Universitat Autònoma de Barcelona, Spain.
Database management.
Pattern recognition.
Optical data processing.
Database Management.
Pattern Recognition.
Image Processing and Computer Vision.