11346nam 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 Computing2278499UNINA03914oam 2200733I 450 991095746140332120251117065812.01-136-72702-71-283-10320-697866131032081-136-72703-50-203-81713-310.4324/9780203817131 (CKB)2670000000091861(EBL)801846(OCoLC)797919186(SSID)ssj0000544574(PQKBManifestationID)12178657(PQKBTitleCode)TC0000544574(PQKBWorkID)10536382(PQKB)10920525(MiAaPQ)EBC801846(MiAaPQ)EBC5292961(Au-PeEL)EBL801846(CaPaEBR)ebr10514282(CaONFJC)MIL2071631(OCoLC)727060706(Au-PeEL)EBL5292961(CaONFJC)MIL310320(EXLCZ)99267000000009186120180706d2011 uy 0engur|n|---|||||txtccrThe politics of religion in South and Southeast Asia /edited by Ishtiaq Ahmed1st ed.Abingdon, Oxon ;New York, N.Y. :Routledge,2011.1 online resource (289 p.)Routledge contemporary Asia series ;32Description based upon print version of record.1-138-78359-5 0-415-60227-0 Includes bibliographical references and index.Cover; The Politics of Religion in South and Southeast Asia; Contents; List of contributors; Preface; 1. The politics of religion in South and Southeast Asia; 2. Religion as a political ideologyin South Asia; 3. Islamism beyond the Islamic heartland: A case study of Bangladesh; 4. Secular versus Hindu nation-building: Dalit, Adivasi, Muslim and Christian experiences in India; 5. Sikh politics and the Indo-Pak relationship; 6. Religious nationalism and minorities in Pakistan: Constitutional and legal bases of discrimination; 7. Women under Islamic Law in Pakistan8. Religion as a political ideology in Southeast Asia9. Political Islam in Indonesia; 10. Religion and politics in the Philippines; 11. Creating a Muslim majority in plural Malaysia:Undermining minority and women's rights; 12. Keeping politics and religion separate in the public square:Managed pluralism and the regulatory state in Singapore; 13. Transnational religious-political movements: Negotiating Hindutva in the diaspora; 14. Negotiating rights through transnational puritan networks: Religious discourses; cyber technology and Pakistani women; IndexThe notion of a 'politics of religion' refers to the increasing role that religion plays in the politics of the contemporary world. This book presents comparative country case studies on the politics of religion in South and South Asia, including India, Pakistan and Indonesia. The politics of religion calls into question the relevance of modernist notions of secularism and democracy, with the emphasis instead on going back to indigenous roots in search of authentic ideologies and models of state and nation building. Within the context of the individual countries, chapters focus on the conseRoutledge contemporary Asia series ;32.Religion and politicsSouth AsiaReligion and politicsSoutheast AsiaSouth AsiaReligionSoutheast AsiaReligionReligion and politicsReligion and politics322/.1095Ahmed Ishtiaq1947-1873357MiAaPQMiAaPQMiAaPQBOOK9910957461403321The politics of religion in South and Southeast Asia4483399UNINA