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The objective of Document Analysis and Recognition (DAR) is to recognize the text and graphicalcomponents of a document and to extract information. With ?rst papers dating back to the 1960’s, DAR is a mature but still gr- ing research?eld with consolidated and known techniques. Optical Character Recognition (OCR) engines are some of the most widely recognized pr- ucts of the research in this ?eld, while broader DAR techniques are nowadays studied and applied to other industrial and o?ce automation systems. In the machine learning community, one of the most widely known - search problems addressed in DAR is recognition of unconstrained handwr- ten characters which has been frequently used in the past as a benchmark for evaluating machine learning algorithms, especially supervised classi?ers. However, developing a DAR system is a complex engineering task that involves the integration of multiple techniques into an organic framework. A reader may feel that the use of machine learning algorithms is not approp- ate for other DAR tasks than character recognition. On the contrary, such algorithms have been massively used for nearly all the tasks in DAR. With large emphasis being devoted to character recognition and word recognition, other tasks such as pre-processing, layout analysis, character segmentation, and signature veri?cation have also bene?ted much from machine learning algorithms.

Machine learning --- Documentation --- Optical pattern recognition --- Apprentissage automatique --- Reconnaissance optique des formes (Informatique) --- Data processing --- Applied Mathematics --- Computer Science --- Civil Engineering --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Machine learning. --- Optical pattern recognition. --- Data processing. --- Learning, Machine --- Engineering. --- Artificial intelligence. --- Applied mathematics. --- Engineering mathematics. --- Appl.Mathematics/Computational Methods of Engineering. --- Artificial Intelligence (incl. Robotics). --- Engineering --- Engineering analysis --- Mathematical analysis --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Construction --- Industrial arts --- Technology --- Mathematics --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Information science --- Information services --- Library science --- Artificial intelligence --- Mathematical and Computational Engineering. --- Artificial Intelligence.

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Engineering sciences. Technology --- Artificial intelligence. Robotics. Simulation. Graphics --- analyse (wiskunde) --- machine learning --- ingenieurswetenschappen --- robots

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Thisvolumecontainspapersselectedforpresentationatthe6thIAPRWorkshop on Document Analysis Systems (DAS 2004) held during September 8–10, 2004 at the University of Florence, Italy. Several papers represent the state of the art in a broad range of “traditional” topics such as layout analysis, applications to graphics recognition, and handwritten documents. Other contributions address the description of complete working systems, which is one of the strengths of this workshop. Some papers extend the application domains to other media, like the processing of Internet documents. The peculiarity of this 6th workshop was the large number of papers related to digital libraries and to the processing of historical documents, a taste which frequently requires the analysis of color documents. A total of 17 papers are associated with these topics, whereas two yearsago (in DAS 2002) only a couple of papers dealt with these problems. In our view there are three main reasons for this new wave in the DAS community. From the scienti?c point of view, several research ?elds reached a thorough knowledge of techniques and problems that can be e?ectively solved, and this expertise can now be applied to new domains. Another incentive has been provided by several research projects funded by the EC and the NSF on topics related to digital libraries.

Computer science. --- Information storage and retrieval systems. --- Computer simulation. --- Computer vision. --- Optical pattern recognition. --- Information systems. --- Computer Science. --- Pattern Recognition. --- Information Storage and Retrieval. --- Image Processing and Computer Vision. --- Simulation and Modeling. --- Computer Appl. in Administrative Data Processing. --- Optical pattern recognition --- Text processing (Computer science) --- Document imaging systems --- Image analysis --- Digital libraries --- Engineering & Applied Sciences --- Applied Physics --- Information storage and retrieval. --- Image processing. --- Pattern recognition. --- Application software. --- Information storage and retrieva. --- Computer modeling --- Computer models --- Modeling, Computer --- Models, Computer --- Simulation, Computer --- Electromechanical analogies --- Mathematical models --- Simulation methods --- Model-integrated computing --- Machine vision --- Vision, Computer --- Artificial intelligence --- Image processing --- Pattern recognition systems --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Automatic data storage --- Automatic information retrieval --- Automation in documentation --- Computer-based information systems --- Data processing systems --- Data storage and retrieval systems --- Discovery systems, Information --- Information discovery systems --- Information processing systems --- Information retrieval systems --- Machine data storage and retrieval --- Mechanized information storage and retrieval systems --- Computer systems --- Electronic information resources --- Data libraries --- Information organization --- Information retrieval --- Optical data processing. --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Computer software --- Optical computing --- Visual data processing --- Bionics --- Electronic data processing --- Integrated optics --- Photonics --- Computers --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Optical equipment --- Pattern perception.

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The second IAPR TC3 Workshop on Arti?cial Neural Networks in Pattern Recognition, ANNPR 2006, was held at the University of Ulm (Germany), - gust 31 - September 2, 2006. The Neural Networks and Computational Intel- gence (TC3) group is one of the 20 Technical Committees of the International AssociationforPatternRegognition(IAPR).ThescopeofTC3includesCom- tational Intelligence approaches, such as fuzzy systems, evolutionary computing and arti?cial neural networks in various pattern recognition applications. AN- NPR 2006 succeeded the outstanding ?rst ANNPR workshop held at the U- versity of Florence in September 2003 and focused on arti?cial neural networks inspired from pattern recognition tasks. In recent years, the ?eld of neural networks has matured considerably in both methodology and real-world applications. As re?ected in this book, art- cial neural networks in pattern recognition combine many ideas from machine learning,advancedstatistics,signalandimageprocessing,andstatisticalpattern recognition for solving complex real-world pattern recognition problems. High quality across such a diverse ?eld of research can only be achieved through a rigorous and selective review process. For this workshop, 49 papers were submitted out of which 26 were selected for inclusion in the proceedings. ANNPR 2006 featured research work in the areas of neural network learning - unsupervised, semi-supervised and supervised - support vector machines, mul- ple classi?er systems, pattern recognition in image processing, and data mining in bioinformatics.

Mathematical statistics --- Biomathematics. Biometry. Biostatistics --- Computer science --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- patroonherkenning --- neuronale netwerken --- ICT (informatie- en communicatietechnieken) --- factoranalyse --- bio-informatica --- informatica --- informatiesystemen --- biometrie --- KI (kunstmatige intelligentie) --- informatica management --- robots

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TheThirdIAPRTC3WorkshoponArti?cialNeuralNetworksinPatternRec- nition, ANNPR 2008, was held at Pierre and Marie Curie University in Paris (France), July 2-4, 2008. The workshop was organized by the Technical C- mittee on Neural Networks and Computational Intelligence (TC3) that is one of the 20 TCs of the International Association for Pattern Recognition (IAPR). The scope of TC3 includes computational intelligence approaches, such as fuzzy systems, evolutionary computing and arti?cial neural networks and their use in various pattern recognition applications. ANNPR 2008 followed the success of the previous workshops: ANNPR 2003 held at the University of Florence (Italy) andANPPR 2006held at ReisensburgCastle, Universityof Ulm (Germany).All the workshops featured a single-track program including both oral sessions and posters with a focus on active participation from every participant. Inrecentyears,the?eld ofneuralnetworkshasmaturedconsiderablyinboth methodologyandreal-worldapplications.Asre?ectedinthisbook,arti?cialn- ral networks in pattern recognition combine many ideas from machine learning, advanced statistics, signal and image processing for solving complex real-world pattern recognition problems. High quality across such a diverse ?eld of research can only be achieved through a rigorous and selective review process. For this workshop, 57 papers were submitted out of which 29 were selected for inclusion in the proceedings. The oral sessions included 18 papers, while 11 contributions were presented as posters. ANNPR 2008 featured research works in the areas of supervised and unsupervised learning, multiple classi?er systems, pattern recognition in signal and image processing, and feature selection.

Pattern recognition systems --- Neural networks (Computer science) --- Artificial intelligence --- Information Technology --- Artificial Intelligence --- Data mining. --- Optical pattern recognition. --- Artificial intelligence. --- Biometrics. --- Data Mining and Knowledge Discovery. --- Computer Engineering. --- Pattern Recognition. --- Artificial Intelligence. --- Information Systems Applications (incl. Internet). --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Computer engineering. --- Pattern recognition. --- Application software. --- Biometrics (Biology). --- Biological statistics --- Biology --- Biometrics (Biology) --- Biostatistics --- Biomathematics --- Statistics --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Computer software --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Computers --- Statistical methods --- Design and construction