LEADER 06173nam 2200817 450 001 9910453376903321 005 20200903223051.0 010 $a981-4368-71-7 035 $a(CKB)2550000001191454 035 $a(EBL)1611949 035 $a(SSID)ssj0000982824 035 $a(PQKBManifestationID)12388972 035 $a(PQKBTitleCode)TC0000982824 035 $a(PQKBWorkID)10987314 035 $a(PQKB)11305523 035 $a(MiAaPQ)EBC1611949 035 $a(WSP)00008280 035 $a(Au-PeEL)EBL1611949 035 $a(CaPaEBR)ebr10832738 035 $a(CaONFJC)MIL570877 035 $a(OCoLC)869905558 035 $a(EXLCZ)992550000001191454 100 $a20130913h20142014 uy| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aAdvances in digital document processing and retrieval /$feditors, Bidyut Baran Chaudhuri (Indian Statistical Institute, India) & Swapan Kumar Parui (Indian Statistical Institute, India) 210 1$aNew Jersey :$cWorld Scientific,$d[2014] 210 4$d©2014 215 $a1 online resource (334 p.) 225 1 $aStatistical science and interdisciplinary research,$x1793-6195 ;$vvolume 13 225 0$aPlatinum jubilee series 300 $aDescription based upon print version of record. 311 $a981-4368-70-9 311 $a1-306-39626-3 320 $aIncludes bibliographical references and index. 327 $aForeword; Preface; Contents; 1. Document Image Analysis using Markovian Models: Application to Historical Documents; 1.1. Introduction; 1.2. Hidden Markov Random Field Models; 1.2.1. Theoretical foundations; 1.2.1.1. Simulated annealing; 1.2.1.2. Iterated Conditional Modes (ICM); 1.2.1.3. Highest Confidence First (HCF) algorithm; 1.2.1.4. 2D Dynamic Programming; 1.2.2. Application of MRF labelling to handwritten document segmentation; 1.2.2.1. Probability densities; 1.2.2.2. Clique potential functions; 1.2.2.3. Observations; 1.2.2.4. Decoding strategy; 1.2.3. Results; 1.2.3.1. Zone labelling 327 $a1.2.3.2. Text line labelling1.2.3.3. Conclusion; 1.3. Conditional Random Field Models; 1.3.1. Proposed model; 1.3.1.1. Feature functions; 1.3.1.2. Model inference; 1.3.1.3. Parameter learning; 1.3.2. A two level CRF model; 1.3.2.1. Observation features; 1.3.2.2. Label features; 1.3.2.3. Learning; 1.3.3. Integrating more contextual information; 1.3.3.1. Global feature function; 1.3.3.2. Combination of the information sources; 1.3.3.3. Linear combination of the information sources (impl.2); 1.3.3.4. Combination of the information sources using an MLP (impl.3); 1.3.4. Experiments and results 327 $a1.4. Conclusions and OutlookAcknowledgments; References; 2. Information Just-in-Time: Going Beyond the Myth of Paperlessness; 2.1. Introduction; 2.2. Information Just-in-Time; 2.2.1. Personal Information Environment; 2.2.2. Hot/Warm/Cold Documents; 2.2.3. Proposed Approach; 2.3. Digital Pen Solution; 2.3.1. Anoto Functionality; 2.3.2. Data Entry Applications; 2.4. iJIT Collaboration Platform; 2.4.1. On-Demand Printing; 2.4.2. Hybrid Document Management System; 2.4.3. Research Notebook Application - iJITNote; 2.4.4. Future Directions; 2.5. Conclusions; Acknowledgments; References 327 $a3. The Role of Document Image Analysis in Trustworthy Elections3.1. Introduction; 3.2. History; 3.3. Problems with Current Voting Technologies; 3.4. Experimental Approaches to Reliable Processing of Voting Records; 3.4.1. Statistical distribution of mark sense errors; 3.4.2. Unbiased context-free visual auditing based on ballot images; 3.4.3. Homogenous class display; 3.4.4. Unique identification of ballots; 3.4.5. Error characteristics of DRE with VVPAT; 3.4.6. Development of testing procedures for voting systems; 3.4.7. Affordances for voters with disabilities; 3.5. Some Related Efforts 327 $a3.6. Concluding RemarksReferences; 4. Information Retrieval from Document Image Databases; 4.1. Introduction; 4.2. Related Work; 4.3. Word Shape Coding; 4.3.1. Word Shape Coding by Character Stroke Categorization; 4.3.2. Word Shape Coding by Character Boundary Extrema; 4.3.3. Word Shape Coding by Character Holes and Reservoirs; 4.4. Document Image Retrieval; 4.4.1. Document Vector Construction; 4.4.2. Document Similarity Measurement; 4.5. Discussions; 4.5.1. Coding Ambiguity; 4.5.2. Coding Robustness; 4.5.3. Document Similarity Measurements; 4.5.4. Coding Scheme Selection; 4.6. Conclusion 327 $aReferences 330 $aFrom the participation of researchers in most important international conferences in the field, it is noted that activities in automatic document processing have been continuously growing. This book is an edited volume in Digital Document Processing where the chapters are written by several internationally renowned researchers in the domain. It will be useful for both students and researchers working on various aspects of document image analysis and recognition problems. It contains chapters on topics that are not covered by any textbook, but are more futuristic like ""Going beyond the Myth of 410 0$aStatistical science and interdisciplinary research ;$vv. 13. 606 $aInformation storage and retrieval systems 606 $aDocumentation$xData processing 606 $aDigital preservation 606 $aDocument imaging systems 606 $aElectronic publishing 606 $aMultimedia systems 606 $aElectronic records 608 $aElectronic books. 615 0$aInformation storage and retrieval systems. 615 0$aDocumentation$xData processing. 615 0$aDigital preservation. 615 0$aDocument imaging systems. 615 0$aElectronic publishing. 615 0$aMultimedia systems. 615 0$aElectronic records. 676 $a025.04 701 $aChaudhuri$b B. B$g(Bidyut Baran)$0948327 701 $aParui$b Swapan Kumar$0948328 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910453376903321 996 $aAdvances in digital document processing and retrieval$92143405 997 $aUNINA LEADER 03320nam 22005535 450 001 9910835058303321 005 20251202133759.0 010 $a3-031-49830-5 024 7 $a10.1007/978-3-031-49830-5 035 $a(MiAaPQ)EBC31150948 035 $a(Au-PeEL)EBL31150948 035 $a(DE-He213)978-3-031-49830-5 035 $a(CKB)30362856500041 035 $a(EXLCZ)9930362856500041 100 $a20240213d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMeasure-Theoretic Probability $eWith Applications to Statistics, Finance, and Engineering /$fby Kenneth Shum 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Birkhäuser,$d2023. 215 $a1 online resource (262 pages) 225 1 $aCompact Textbooks in Mathematics,$x2296-455X 311 08$a3-031-49832-1 311 08$a3-031-49829-1 327 $aPreface -- Beyond discrete and continuous random variables -- Probability spaces -- Lebesgue?Stieltjes measures -- Measurable functions and random variables -- Statistical independence -- Lebesgue integral and mathematical expectation -- Properties of Lebesgue integral and convergence theorems -- Product space and coupling -- Moment generating functions and characteristic functions -- Modes of convergence -- Laws of large numbers -- Techniques from Hilbert space theory -- Conditional expectation -- Levy?s continuity theorem and central limit theorem -- References -- Index. 330 $aThis textbook offers an approachable introduction to measure-theoretic probability, illustrating core concepts with examples from statistics and engineering. The author presents complex concepts in a succinct manner, making otherwise intimidating material approachable to undergraduates who are not necessarily studying mathematics as their major. Throughout, readers will learn how probability serves as the language in a variety of exciting fields. Specific applications covered include the coupon collector?s problem, Monte Carlo integration in finance, data compression in information theory, and more. Measure-Theoretic Probability is ideal for a one-semester course and will best suit undergraduates studying statistics, data science, financial engineering, and economics who want to understand and apply more advanced ideas from probability to their disciplines. As a concise and rigorous introduction to measure-theoretic probability, it is also suitable for self-study. Prerequisites include a basic knowledge of probability and elementary concepts from real analysis. 410 0$aCompact Textbooks in Mathematics,$x2296-455X 606 $aProbabilities 606 $aMeasure theory 606 $aProbability Theory 606 $aApplied Probability 606 $aMeasure and Integration 615 0$aProbabilities. 615 0$aMeasure theory. 615 14$aProbability Theory. 615 24$aApplied Probability. 615 24$aMeasure and Integration. 676 $a519.2 700 $aShum$b Kenneth$01741670 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910835058303321 996 $aMeasure-Theoretic Probability$94167788 997 $aUNINA