04125oam 2200697I 450 991045772220332120200520144314.01-315-69818-81-280-91221-997866109122160-7656-2172-X10.4324/9781315698182 (CKB)1000000000348503(EBL)302499(OCoLC)476082101(SSID)ssj0000271152(PQKBManifestationID)11211732(PQKBTitleCode)TC0000271152(PQKBWorkID)10299267(PQKB)11370174(MiAaPQ)EBC302499(Au-PeEL)EBL302499(CaPaEBR)ebr10178115(CaONFJC)MIL91221(OCoLC)958106551(EXLCZ)99100000000034850320180706e20152006 uy 0engur|n|---|||||txtccrWhere are poor people to live? transforming public housing communities /Larry Bennett, Janet L. Smith, and Patricia A. Wright, editorsLondon ;New York :Routledge,2015.1 online resource (344 p.)Cities and contemporary societyFirst published 2006 by M.E. Sharpe.0-7656-1076-0 0-7656-1075-2 Includes bibliographical references and index."Our fight must go on" / Rene Maxwell. -- Introduction / Larry Bennett, Janet L. Smith, and Patricia A. Wright. -- I. National and local context for public housing transformation. Public housing transformation: evolving national policy / Janet L. Smith ; Public housing's Cinderella: policy dynamics of HOPE VI in the mid-1990s / Yan Zhang and Gretchen Weismann ; The HOPE VI program: what has happened to the residents? / Susan J. Popkin. -- II. On the ground in Chicago: reshaping public housing communities. The Chicago Housing Authority's plan for transformation / Janet L. Smith ; Community resistance to CHA transformation: the history, evolution, struggles, and accomplishments of the Coalition to Protect Public Housing / Patricia A. Wright ; The case of Cabrini-Green / Patrica A. Wright, with Richard M. Wheelock and Carol Steele ; A critical analysis of the ABLA redevelopment plan / Larry Bennett, Nancy Hudspeth, and Patricia A. Wright ; Relocated public housing residents have little hope of returning: work requirements for mixed-income public housing developments / William P. Wilen and Rajesh D. Nayak. -- III. Learning from Chicago: prospects and challenges for policy makers. Gautreaux and Chicago's public housing crisis: the conflict between achieving integration and providing decent housing for very low-income African Americans / William P. Wilen and Wendy L. Stasell ; Mixed-income communities: designing out poverty or pushing out the poor? / Janet L. Smith ; Downtown restructuring and public housing in contemporary Chicago: fashioning a better world-class city / Larry Bennett. -- Epilogue / Larry Bennett, Janet L. Smith, and Patricia A. Wright.Shows how major shifts in federal policy are spurring local public housing authorities to demolish their high-rise, low-income developments, and replace them with affordable low-rise, mixed income communities. This book focuses on Chicago, and that city's affordable housing crisis.Cities and contemporary society.Public housingIllinoisChicagoLow-income housingIllinoisChicagoPublic housingGovernment policyUnited StatesElectronic books.Public housingLow-income housingPublic housingGovernment policy363.5/850977311Bennett Larry1950-887353Smith Janet L.1962-901495Wright Patricia A901496MiAaPQMiAaPQMiAaPQBOOK9910457722203321Where are poor people to live2014904UNINA11346nam 22007455 450 991025435530332120200702073258.03-319-50252-210.1007/978-3-319-50252-6(CKB)3710000001006453(DE-He213)978-3-319-50252-6(MiAaPQ)EBC6283800(MiAaPQ)EBC5591959(Au-PeEL)EBL5591959(OCoLC)1066197241(PPN)197455409(EXLCZ)99371000000100645320161223d2017 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierOptical Character Recognition Systems for Different Languages with Soft Computing /by Arindam Chaudhuri, Krupa Mandaviya, Pratixa Badelia, Soumya K Ghosh1st ed. 2017.Cham :Springer International Publishing :Imprint: Springer,2017.1 online resource (XIX, 248 p. 95 illus.) Studies in Fuzziness and Soft Computing,1434-9922 ;3523-319-50251-4 Includes bibliographical references and index.Intro -- Contents -- List of Figures -- List of Tables -- 1 Introduction -- 1.1 Organization of the Monograph -- 1.2 Notation -- 1.3 State of Art -- 1.4 Research Issues and Challenges -- 1.5 Figures -- 1.6 MATLAB OCR Toolbox -- References -- 2 Optical Character Recognition Systems -- Abstract -- 2.1 Introduction -- 2.2 Optical Character Recognition Systems: Background and History -- 2.3 Techniques of Optical Character Recognition Systems -- 2.3.1 Optical Scanning -- 2.3.2 Location Segmentation -- 2.3.3 Pre-processing -- 2.3.4 Segmentation -- 2.3.5 Representation -- 2.3.6 Feature Extraction -- 2.3.7 Training and Recognition -- 2.3.8 Post-processing -- 2.4 Applications of Optical Character Recognition Systems -- 2.5 Status of Optical Character Recognition Systems -- 2.6 Future of Optical Character Recognition Systems -- References -- 3 Soft Computing Techniques for Optical Character Recognition Systems -- Abstract -- 3.1 Introduction -- 3.2 Soft Computing Constituents -- 3.2.1 Fuzzy Sets -- 3.2.2 Artificial Neural Networks -- 3.2.3 Genetic Algorithms -- 3.2.4 Rough Sets -- 3.3 Hough Transform for Fuzzy Feature Extraction -- 3.4 Genetic Algorithms for Feature Selection -- 3.5 Rough Fuzzy Multilayer Perceptron -- 3.6 Fuzzy and Fuzzy Rough Support Vector Machines -- 3.7 Hierarchical Fuzzy Bidirectional Recurrent Neural Networks -- 3.8 Fuzzy Markov Random Fields -- 3.9 Other Soft Computing Techniques -- References -- 4 Optical Character Recognition Systems for English Language -- Abstract -- 4.1 Introduction -- 4.2 English Language Script and Experimental Dataset -- 4.3 Challenges of Optical Character Recognition Systems for English Language -- 4.4 Data Acquisition -- 4.5 Data Pre-processing -- 4.5.1 Binarization -- 4.5.2 Noise Removal -- 4.5.3 Skew Detection and Correction -- 4.5.4 Character Segmentation -- 4.5.5 Thinning -- 4.6 Feature Extraction.4.7 Feature Based Classification: Sate of Art -- 4.7.1 Feature Based Classification Through Fuzzy Multilayer Perceptron -- 4.7.2 Feature Based Classification Through Rough Fuzzy Multilayer Perceptron -- 4.7.3 Feature Based Classification Through Fuzzy and Fuzzy Rough Support Vector Machines -- 4.8 Experimental Results -- 4.8.1 Fuzzy Multilayer Perceptron -- 4.8.2 Rough Fuzzy Multilayer Perceptron -- 4.8.3 Fuzzy and Fuzzy Rough Support Vector Machines -- 4.9 Further Discussions -- References -- 5 Optical Character Recognition Systems for French Language -- Abstract -- 5.1 Introduction -- 5.2 French Language Script and Experimental Dataset -- 5.3 Challenges of Optical Character Recognition Systems for French Language -- 5.4 Data Acquisition -- 5.5 Data Pre-processing -- 5.5.1 Text Region Extraction -- 5.5.2 Skew Detection and Correction -- 5.5.3 Binarization -- 5.5.4 Noise Removal -- 5.5.5 Character Segmentation -- 5.5.6 Thinning -- 5.6 Feature Extraction Through Fuzzy Hough Transform -- 5.7 Feature Based Classification: Sate of Art -- 5.7.1 Feature Based Classification Through Rough Fuzzy Multilayer Perceptron -- 5.7.2 Feature Based Classification Through Fuzzy and Fuzzy Rough Support Vector Machines -- 5.7.3 Feature Based Classification Through Hierarchical Fuzzy Bidirectional Recurrent Neural Networks -- 5.8 Experimental Results -- 5.8.1 Rough Fuzzy Multilayer Perceptron -- 5.8.2 Fuzzy and Fuzzy Rough Support Vector Machines -- 5.8.3 Hierarchical Fuzzy Bidirectional Recurrent Neural Networks -- 5.9 Further Discussions -- References -- 6 Optical Character Recognition Systems for German Language -- Abstract -- 6.1 Introduction -- 6.2 German Language Script and Experimental Dataset -- 6.3 Challenges of Optical Character Recognition Systems for German Language -- 6.4 Data Acquisition -- 6.5 Data Pre-processing -- 6.5.1 Text Region Extraction.6.5.2 Skew Detection and Correction -- 6.5.3 Binarization -- 6.5.4 Noise Removal -- 6.5.5 Character Segmentation -- 6.5.6 Thinning -- 6.6 Feature Selection Through Genetic Algorithms -- 6.7 Feature Based Classification: Sate of Art -- 6.7.1 Feature Based Classification Through Rough Fuzzy Multilayer Perceptron -- 6.7.2 Feature Based Classification Through Fuzzy and Fuzzy Rough Support Vector Machines -- 6.7.3 Feature Based Classification Through Hierarchical Fuzzy Bidirectional Recurrent Neural Networks -- 6.8 Experimental Results -- 6.8.1 Rough Fuzzy Multilayer Perceptron -- 6.8.2 Fuzzy and Fuzzy Rough Support Vector Machines -- 6.8.3 Hierarchical Fuzzy Bidirectional Recurrent Neural Networks -- 6.9 Further Discussions -- References -- 7 Optical Character Recognition Systems for Latin Language -- Abstract -- 7.1 Introduction -- 7.2 Latin Language Script and Experimental Dataset -- 7.3 Challenges of Optical Character Recognition Systems for Latin Language -- 7.4 Data Acquisition -- 7.5 Data Pre-processing -- 7.5.1 Text Region Extraction -- 7.5.2 Skew Detection and Correction -- 7.5.3 Binarization -- 7.5.4 Noise Removal -- 7.5.5 Character Segmentation -- 7.5.6 Thinning -- 7.6 Feature Selection Through Genetic Algorithms -- 7.7 Feature Based Classification: Sate of Art -- 7.7.1 Feature Based Classification Through Rough Fuzzy Multilayer Perceptron -- 7.7.2 Feature Based Classification Through Fuzzy and Fuzzy Rough Support Vector Machines -- 7.7.3 Feature Based Classification Through Hierarchical Fuzzy Rough Bidirectional Recurrent Neural Networks -- 7.8 Experimental Results -- 7.8.1 Rough Fuzzy Multilayer Perceptron -- 7.8.2 Fuzzy and Fuzzy Rough Support Vector Machines -- 7.8.3 Hierarchical Fuzzy Rough Bidirectional Recurrent Neural Networks -- 7.9 Further Discussions -- References -- 8 Optical Character Recognition Systems for Hindi Language.Abstract -- 8.1 Introduction -- 8.2 Hindi Language Script and Experimental Dataset -- 8.3 Challenges of Optical Character Recognition Systems for Hindi Language -- 8.4 Data Acquisition -- 8.5 Data Pre-processing -- 8.5.1 Binarization -- 8.5.2 Noise Removal -- 8.5.3 Skew Detection and Correction -- 8.5.4 Character Segmentation -- 8.5.5 Thinning -- 8.6 Feature Extraction Through Hough Transform -- 8.7 Feature Based Classification: Sate of Art -- 8.7.1 Feature Based Classification Through Rough Fuzzy Multilayer Perceptron -- 8.7.2 Feature Based Classification Through Fuzzy and Fuzzy Rough Support Vector Machines -- 8.7.3 Feature Based Classification Through Fuzzy Markov Random Fields -- 8.8 Experimental Results -- 8.8.1 Rough Fuzzy Multilayer Perceptron -- 8.8.2 Fuzzy and Fuzzy Rough Support Vector Machines -- 8.8.3 Fuzzy Markov Random Fields -- 8.9 Further Discussions -- References -- 9 Optical Character Recognition Systems for Gujrati Language -- Abstract -- 9.1 Introduction -- 9.2 Gujrati Language Script and Experimental Dataset -- 9.3 Challenges of Optical Character Recognition Systems for Gujrati Language -- 9.4 Data Acquisition -- 9.5 Data Pre-processing -- 9.5.1 Binarization -- 9.5.2 Noise Removal -- 9.5.3 Skew Detection and Correction -- 9.5.4 Character Segmentation -- 9.5.5 Thinning -- 9.6 Feature Selection Through Genetic Algorithms -- 9.7 Feature Based Classification: Sate of Art -- 9.7.1 Feature Based Classification Through Rough Fuzzy Multilayer Perceptron -- 9.7.2 Feature Based Classification Through Fuzzy and Fuzzy Rough Support Vector Machines -- 9.7.3 Feature Based Classification Through Fuzzy Markov Random Fields -- 9.8 Experimental Results -- 9.8.1 Rough Fuzzy Multilayer Perceptron -- 9.8.2 Fuzzy and Fuzzy Rough Support Vector Machines -- 9.8.3 Fuzzy Markov Random Fields -- 9.9 Further Discussions -- References.10 Summary and Future Research -- 10.1 Summary -- 10.2 Future Research -- References -- Index.The book offers a comprehensive survey of soft-computing models for optical character recognition systems. The various techniques, including fuzzy and rough sets, artificial neural networks and genetic algorithms, are tested using real texts written in different languages, such as English, French, German, Latin, Hindi and Gujrati, which have been extracted by publicly available datasets. The simulation studies, which are reported in details here, show that soft-computing based modeling of OCR systems performs consistently better than traditional models. Mainly intended as state-of-the-art survey for postgraduates and researchers in pattern recognition, optical character recognition and soft computing, this book will be useful for professionals in computer vision and image processing alike, dealing with different issues related to optical character recognition.Studies in Fuzziness and Soft Computing,1434-9922 ;352Computational intelligencePattern perceptionComputational linguisticsArtificial intelligenceComputational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Pattern Recognitionhttps://scigraph.springernature.com/ontologies/product-market-codes/I2203XComputational Linguisticshttps://scigraph.springernature.com/ontologies/product-market-codes/N22000Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Computational intelligence.Pattern perception.Computational linguistics.Artificial intelligence.Computational Intelligence.Pattern Recognition.Computational Linguistics.Artificial Intelligence.006.424Chaudhuri Arindamauthttp://id.loc.gov/vocabulary/relators/aut763017Mandaviya Krupaauthttp://id.loc.gov/vocabulary/relators/autBadelia Pratixaauthttp://id.loc.gov/vocabulary/relators/autK Ghosh Soumyaauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910254355303321Optical Character Recognition Systems for Different Languages with Soft Computing2278499UNINA