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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.

Computer graphics. --- Digital libraries. --- Image analysis. --- Pattern recognition systems -- Data processing. --- Signs and symbols -- Data processing. --- Digital libraries --- Image analysis --- Computer graphics --- Pattern recognition systems --- Signs and symbols --- Electrical & Computer Engineering --- Social Sciences --- Engineering & Applied Sciences --- Electrical Engineering --- Computer Science --- Library & Information Science --- Data processing --- Computers. --- Technology. --- Applied science --- Arts, Useful --- Science, Applied --- Useful arts --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic brains --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer science. --- Database management. --- Image processing. --- Pattern recognition. --- Computer Science. --- Database Management. --- Pattern Recognition. --- Image Processing and Computer Vision. --- Science --- Industrial arts --- Material culture --- Computer systems --- Cybernetics --- Machine theory --- Calculators --- Cyberspace --- Optical pattern recognition. --- Computer vision. --- Machine vision --- Vision, Computer --- Artificial intelligence --- Image processing --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Data base management --- Data services (Database management) --- Database management services --- DBMS (Computer science) --- Generalized data management systems --- Services, Database management --- Systems, Database management --- Systems, Generalized database management --- Electronic data processing --- Optical data processing. --- Optical computing --- Visual data processing --- Bionics --- Integrated optics --- Photonics --- Computers --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Optical equipment