04745nam 22007094a 450 991045456660332120200520144314.00-520-92381-21-282-35645-397866123564521-59734-678-010.1525/9780520923812(CKB)111056485639880(EBL)223600(OCoLC)475928531(SSID)ssj0000179688(PQKBManifestationID)11183115(PQKBTitleCode)TC0000179688(PQKBWorkID)10138727(PQKB)11011016(StDuBDS)EDZ0000055919(MiAaPQ)EBC223600(OCoLC)49570022(MdBmJHUP)muse30584(DE-B1597)519666(DE-B1597)9780520923812(Au-PeEL)EBL223600(CaPaEBR)ebr10054460(CaONFJC)MIL235645(EXLCZ)9911105648563988020000404d2000 uy 0engur|||||||nn|ntxtccrInsatiable appetite[electronic resource] the United States and the ecological degradation of the tropical world /Richard P. TuckerBerkeley University of California Pressc20001 online resource (567 p.)Description based upon print version of record.0-520-22087-0 Includes bibliographical references (p. 437-524) and index.Front matter --Contents --Acknowledgments --Introduction --1. America's Sweet Tooth: The Sugar Trust And The Caribbean Lowlands --2. Lords Of The Pacific: Sugar Barons In The Hawaiian And Philippine Islands --3. Banana Republics: Yankee Fruit Companies And The Tropical American Lowlands --4. The Last Drop: The American Coffee Market And The Hill Regions Of Latin America --5. The Tropical Cost Of The Automotive Age: Corporate Rubber Empires And The Rainforest --6. The Crop On Hooves: Yankee Interests In Tropical Cattle Ranching --7. Unsustainable Yield: American Foresters And Tropical Timber Resources --Conclusion --Appendix --Notes --Bibliography --IndexIn the late 1800's American entrepreneurs became participants in the 400-year history of European economic and ecological hegemony in the tropics. Beginning as buyers in the tropical ports of the Atlantic and Pacific, they evolved into land speculators, controlling and managing the areas where tropical crops were grown for carefully fostered consumer markets at home. As corporate agro-industry emerged, the speculators took direct control of the ecological destinies of many tropical lands. Supported by the U.S. government's diplomatic and military protection, they migrated and built private empires in the Caribbean, Central and South America, the Pacific, Southeast Asia, and West Africa. Yankee investors and plantation managers mobilized engineers, agronomists, and loggers to undertake what they called the "Conquest of the Tropics," claiming to bring civilization to benighted peoples and cultivation to unproductive nature. In competitive cooperation with local landed and political elites, they not only cleared natural forests but also displaced multicrop tribal and peasant lands with monocrop export plantations rooted in private property regimes. This book is a rich history of the transformation of the tropics in modern times, pointing ultimately to the declining biodiversity that has resulted from the domestication of widely varied natural systems. Richard P. Tucker graphically illustrates his study with six major crops, each a virtual empire in itself-sugar, bananas, coffee, rubber, beef, and timber. He concludes that as long as corporate-dominated free trade is ascendant, paying little heed to its long-term ecological consequences, the health of the tropical world is gravely endangered.Tropical cropsEconomic aspectsHistory20th centuryTropical cropsEnvironmental aspectsHistory20th centuryInvestments, AmericanTropicsHistory20th centuryEnvironmental degradationTropicsHistory20th centuryElectronic books.Tropical cropsEconomic aspectsHistoryTropical cropsEnvironmental aspectsHistoryInvestments, AmericanHistoryEnvironmental degradationHistory333.7/0913Tucker Richard P.1938-1016746MiAaPQMiAaPQMiAaPQBOOK9910454566603321Insatiable appetite2380608UNINA11346nam 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