Advances in Digital Document Processing and Retrieval.

By: Al, Chaudhuri Bidyut Baran EtContributor(s): Chaudhuri, Bidyut Baran | Parui, Swapan KumarMaterial type: TextTextSeries: Statistical Science and Interdisciplinary Research SerPublisher: Singapore : World Scientific Publishing Co Pte Ltd, 2013Copyright date: ©2013Description: 1 online resource (334 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9789814368711Subject(s): Digital preservation | Document imaging systems | Documentation -- Data processing | Electronic publishing | Electronic records | Information storage and retrieval systems | Multimedia systemsGenre/Form: Electronic books.Additional physical formats: Print version:: Advances in Digital Document Processing and RetrievalDDC classification: 025.04 LOC classification: Z699.A38 2014ebOnline resources: Click to View
Contents:
Intro -- Contents -- Foreword -- Preface -- 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 Document Image Analysis using Markovian Models: Application to Historical Documents -- 1.1.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 -- 1.2.3.2. Text line labelling -- 1.2.3.3. 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 -- 1.4. Conclusions and Outlook -- Acknowledgments -- 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 -- 3. The Role of Document Image Analysis in Trustworthy Elections -- 3.1. 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 -- 3.6. Concluding Remarks -- References -- 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 -- References -- 5. Indexing and Retrieval of Handwritten Documents -- 5.1. Introduction -- 5.2. Template-based keyword spotting -- 5.2.1. DTW based keyword spotting -- 5.2.2. GSC feature based keyword spotting -- 5.3. Template-free keyword spotting -- 5.3.1. Segmentation of word image into character images -- 5.3.2. Extracting Gabor features from character image -- 5.3.3. Character classification probability Pr(ci|Vi) -- 5.3.4. Experimental results -- 5.4. Handwritten document retrieval -- 5.5. Summary -- References -- 6. Comprehensive Check Image Reader -- 6.1. Introduction.
6.2. Check Preprocessing -- 6.2.1. Check processing challenges -- 6.2.1.1. Legal amount recognition challenge -- 6.2.1.2. Check processing challenges -- 6.2.1.3. Date recognition challenge -- 6.2.1.4. Payee name recognition challenge -- 6.2.1.5. Signature detection challenge -- 6.2.1.6. Courtesy amount field -- 6.3. Legal Amount Recognition -- 6.3.1. Legal line parsing -- 6.3.1.1. From word to legal line phrase matching -- 6.3.2. Legal line recognition for handwritten checks -- 6.3.3. Handwritten word recognition -- 6.3.3.1. Slant correction -- 6.3.3.2. Character segmentation -- 6.3.3.3. Word matching algorithm -- 6.3.3.4. Legal line phrase matching -- 6.4. Experimental Results for CAR and LAR -- 6.5. Automatic Signature Verification -- 6.5.1. Feature-based ASV -- 6.5.1.1. Description of features -- 6.5.2. ASV based on dynamic feature matching -- 6.5.3. Multi-references ASV matching function -- 6.5.4. Results -- 6.6. Conclusion -- Acknowledgments -- References -- 7. Statistical Deformation Model for Handwritten Character Recognition -- 7.1. Introduction -- 7.1.1. Contributions of this Chapter -- 7.2. Statistical Deformation Model of Offline Handwritten Character Recognition -- 7.2.1. Extraction of Deformations by Elastic Matching -- 7.2.2. Estimations of Eigen-Deformations -- 7.2.3. Recognition with Eigen-Deformations (1) -- 7.2.4. Recognition with Eigen-Deformations (2) -- 7.2.5. Recognition Result -- 7.2.6. Related Work -- 7.3. Statistical Deformation Model of Online Handwritten Character Recognition -- 7.3.1. Extraction of Deformations by Elastic Matching -- 7.3.2. Estimation of Eigen-Deformations -- 7.3.3. Recognition with Eigen-Deformations -- 7.3.4. Recognition Results -- 7.3.5. Related Work -- 7.4. Conclusion -- Acknowledgment -- References -- 8. Robust Word Recognition for Museum Index Cards with the SNT-Grid -- 8.1. Introduction.
8.2. Synthetic Training Data -- 8.3. The Scanning N-Tuple Grid -- 8.3.1. Training and recognition of the SNT-Grid -- 8.3.2. Fast encoding with a two pass templates -- 8.4. Isolated Characters Results in Cards with Synthetic Data -- 8.5. Priority Queue Method -- 8.6. Experimental Results -- 8.6.1. Mis-recognized examples -- 8.6.2. Correctly recognized examples -- 8.7. Discussion and Conclusions -- References -- 9. Historical Handwritten Document Recognition -- 9.1. Introduction -- 9.2. Related Work on Historical Handwritten Document Recognition and Retrieval -- 9.3. Classification Models for Handwritten Word Recognition -- 9.3.1. Support Vector Machines -- 9.3.2. Conditional Maximum Entropy Models -- 9.3.2.1. Discrete Predicates -- 9.3.2.2. Continuous Predicates -- 9.3.3. Naive Bayes with Gaussian Kernel Density Estimate -- 9.4. Sequence Models for Word Recognition -- 9.4.1. Word Recognition with Discrete HMMs -- 9.4.1.1. Feature Probability Smoothing for HMMs -- 9.4.2. Conditional Random Fields Framework -- 9.4.2.1. Inference and Training in CRFs -- 9.4.2.2. Training and Inference with Beam Search -- 9.4.2.3. Word Recognition with CRFs -- 9.4.3. HMM with Gaussian Kernel Density Estimates -- 9.5. Experimental Results -- 9.5.1. Experimental Setup -- 9.5.2. Results on Different Classification Models -- 9.5.2.1. SVMs -- 9.5.2.2. Conditional Maximum Entropy Models -- 9.5.2.3. Naive Bayes with Gaussian Density Estimate -- 9.5.3. Tune and Compare Beam Search for Our CRF Model -- 9.5.4. Result Comparisons -- Acknowledgments -- References -- 10. Statistical Modeling of Document Appearance -- 10.1. Statistical appearance models -- 10.2. Layout grammars and parsing -- 10.2.1. Computational challenge -- 10.2.1.1. Exponential number of segmentations or groupings -- 10.2.1.2. Context sensitivity -- 10.2.2. Generative context free and attribute grammars.
10.2.3. Non-generative grammars -- 10.3. Probabilistic Markov grammars -- 10.3.1. Separable layouts and Document Image Decoding -- 10.3.2. Turbo recognition -- 10.4. Local feature based models -- 10.4.1. Features measured in subregions -- 10.4.2. Local descriptors -- 10.4.3. Locational distribution of intensity variations -- 10.4.4. Feature descriptors without location -- 10.5. Graph representations of layout -- 10.5.1. Hidden tree Markov model -- 10.6. Models of multi-line structure -- 10.6.1. Tables -- 10.6.2. Mathematical expressions -- 10.7. Models of textline structure -- 10.7.1. Baseline skew and warping -- 10.7.2. Glyph layout on baseline -- 10.7.3. HMMs for textlines -- 10.8. Models of character and stroke appearance -- 10.9. Research frontiers -- References -- 11. Reverse-Engineering of PDF Files -- 11.1. Introduction -- 11.2. Related Works -- 11.2.1. Related Tools -- 11.2.2. Related Researches -- 11.3. PDF File Format -- 11.3.1. Internal File Structure -- 11.3.2. Content Streams -- 11.3.2.1. Text Representation -- 11.3.2.2. Graphics Representation -- 11.3.2.3. Images Representation -- 11.3.3. Structuring the Content -- 11.4. Reverse Engineering Approach -- 11.4.1. Physical vs. Logical Structures -- 11.4.2. Global Architecture -- 11.5. XCDF Canonical Format -- 11.5.1. Requirements -- 11.5.2. Format Description -- 11.5.3. Representation of Text Blocks -- 11.5.4. Representation of Vector Graphics -- 11.5.5. Representation of Images -- 11.5.6. Space Coordinates and Device Independence -- 11.6. Document Analysis with XED -- 11.6.1. Parsing PDF Objects -- 11.6.2. Creating the Virtual Document -- 11.6.3. Layout Analysis -- 11.6.4. Evaluation of XED's Physical Structures Extraction -- 11.7. Document Understanding with Dolores -- 11.7.1. Logical Structures and Document Models -- 11.7.2. A First Step Toward Document Reconstruction.
11.7.3. Recovering the Logical Structure from Newspapers.
Summary: From 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 Paperlessness", or interesting application areas like "The Role of Document Image Analysis in Trustworthy Elections" as well as "Word Recognition for Museum Index Cards with SNT-Grid". Persons developing document analysis software for industry may also find the chapters useful and attractive. The language of the chapters is simple and clear, along with drawings/diagrams wherever necessary. An adequate number of references are given at the end of each chapter. Overall, the book is highly readable and will be an asset to the community. Renowned contributors include George Nagy, Hiromichi Fujisawa, F Kimura, D Lopresti, Chew Lim Tan, S Uchida, Thierry Paquet, Laurent Heutte, V Govindaraju, R Manmatha.
Tags from this library: No tags from this library for this title. Log in to add tags.
    Average rating: 0.0 (0 votes)
No physical items for this record

Intro -- Contents -- Foreword -- Preface -- 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 -- 1.2.3.2. Text line labelling -- 1.2.3.3. 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 -- 1.4. Conclusions and Outlook -- Acknowledgments -- 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 -- 3. The Role of Document Image Analysis in Trustworthy Elections -- 3.1. 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 -- 3.6. Concluding Remarks -- References -- 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 -- References -- 5. Indexing and Retrieval of Handwritten Documents -- 5.1. Introduction -- 5.2. Template-based keyword spotting -- 5.2.1. DTW based keyword spotting -- 5.2.2. GSC feature based keyword spotting -- 5.3. Template-free keyword spotting -- 5.3.1. Segmentation of word image into character images -- 5.3.2. Extracting Gabor features from character image -- 5.3.3. Character classification probability Pr(ci|Vi) -- 5.3.4. Experimental results -- 5.4. Handwritten document retrieval -- 5.5. Summary -- References -- 6. Comprehensive Check Image Reader -- 6.1. Introduction.

6.2. Check Preprocessing -- 6.2.1. Check processing challenges -- 6.2.1.1. Legal amount recognition challenge -- 6.2.1.2. Check processing challenges -- 6.2.1.3. Date recognition challenge -- 6.2.1.4. Payee name recognition challenge -- 6.2.1.5. Signature detection challenge -- 6.2.1.6. Courtesy amount field -- 6.3. Legal Amount Recognition -- 6.3.1. Legal line parsing -- 6.3.1.1. From word to legal line phrase matching -- 6.3.2. Legal line recognition for handwritten checks -- 6.3.3. Handwritten word recognition -- 6.3.3.1. Slant correction -- 6.3.3.2. Character segmentation -- 6.3.3.3. Word matching algorithm -- 6.3.3.4. Legal line phrase matching -- 6.4. Experimental Results for CAR and LAR -- 6.5. Automatic Signature Verification -- 6.5.1. Feature-based ASV -- 6.5.1.1. Description of features -- 6.5.2. ASV based on dynamic feature matching -- 6.5.3. Multi-references ASV matching function -- 6.5.4. Results -- 6.6. Conclusion -- Acknowledgments -- References -- 7. Statistical Deformation Model for Handwritten Character Recognition -- 7.1. Introduction -- 7.1.1. Contributions of this Chapter -- 7.2. Statistical Deformation Model of Offline Handwritten Character Recognition -- 7.2.1. Extraction of Deformations by Elastic Matching -- 7.2.2. Estimations of Eigen-Deformations -- 7.2.3. Recognition with Eigen-Deformations (1) -- 7.2.4. Recognition with Eigen-Deformations (2) -- 7.2.5. Recognition Result -- 7.2.6. Related Work -- 7.3. Statistical Deformation Model of Online Handwritten Character Recognition -- 7.3.1. Extraction of Deformations by Elastic Matching -- 7.3.2. Estimation of Eigen-Deformations -- 7.3.3. Recognition with Eigen-Deformations -- 7.3.4. Recognition Results -- 7.3.5. Related Work -- 7.4. Conclusion -- Acknowledgment -- References -- 8. Robust Word Recognition for Museum Index Cards with the SNT-Grid -- 8.1. Introduction.

8.2. Synthetic Training Data -- 8.3. The Scanning N-Tuple Grid -- 8.3.1. Training and recognition of the SNT-Grid -- 8.3.2. Fast encoding with a two pass templates -- 8.4. Isolated Characters Results in Cards with Synthetic Data -- 8.5. Priority Queue Method -- 8.6. Experimental Results -- 8.6.1. Mis-recognized examples -- 8.6.2. Correctly recognized examples -- 8.7. Discussion and Conclusions -- References -- 9. Historical Handwritten Document Recognition -- 9.1. Introduction -- 9.2. Related Work on Historical Handwritten Document Recognition and Retrieval -- 9.3. Classification Models for Handwritten Word Recognition -- 9.3.1. Support Vector Machines -- 9.3.2. Conditional Maximum Entropy Models -- 9.3.2.1. Discrete Predicates -- 9.3.2.2. Continuous Predicates -- 9.3.3. Naive Bayes with Gaussian Kernel Density Estimate -- 9.4. Sequence Models for Word Recognition -- 9.4.1. Word Recognition with Discrete HMMs -- 9.4.1.1. Feature Probability Smoothing for HMMs -- 9.4.2. Conditional Random Fields Framework -- 9.4.2.1. Inference and Training in CRFs -- 9.4.2.2. Training and Inference with Beam Search -- 9.4.2.3. Word Recognition with CRFs -- 9.4.3. HMM with Gaussian Kernel Density Estimates -- 9.5. Experimental Results -- 9.5.1. Experimental Setup -- 9.5.2. Results on Different Classification Models -- 9.5.2.1. SVMs -- 9.5.2.2. Conditional Maximum Entropy Models -- 9.5.2.3. Naive Bayes with Gaussian Density Estimate -- 9.5.3. Tune and Compare Beam Search for Our CRF Model -- 9.5.4. Result Comparisons -- Acknowledgments -- References -- 10. Statistical Modeling of Document Appearance -- 10.1. Statistical appearance models -- 10.2. Layout grammars and parsing -- 10.2.1. Computational challenge -- 10.2.1.1. Exponential number of segmentations or groupings -- 10.2.1.2. Context sensitivity -- 10.2.2. Generative context free and attribute grammars.

10.2.3. Non-generative grammars -- 10.3. Probabilistic Markov grammars -- 10.3.1. Separable layouts and Document Image Decoding -- 10.3.2. Turbo recognition -- 10.4. Local feature based models -- 10.4.1. Features measured in subregions -- 10.4.2. Local descriptors -- 10.4.3. Locational distribution of intensity variations -- 10.4.4. Feature descriptors without location -- 10.5. Graph representations of layout -- 10.5.1. Hidden tree Markov model -- 10.6. Models of multi-line structure -- 10.6.1. Tables -- 10.6.2. Mathematical expressions -- 10.7. Models of textline structure -- 10.7.1. Baseline skew and warping -- 10.7.2. Glyph layout on baseline -- 10.7.3. HMMs for textlines -- 10.8. Models of character and stroke appearance -- 10.9. Research frontiers -- References -- 11. Reverse-Engineering of PDF Files -- 11.1. Introduction -- 11.2. Related Works -- 11.2.1. Related Tools -- 11.2.2. Related Researches -- 11.3. PDF File Format -- 11.3.1. Internal File Structure -- 11.3.2. Content Streams -- 11.3.2.1. Text Representation -- 11.3.2.2. Graphics Representation -- 11.3.2.3. Images Representation -- 11.3.3. Structuring the Content -- 11.4. Reverse Engineering Approach -- 11.4.1. Physical vs. Logical Structures -- 11.4.2. Global Architecture -- 11.5. XCDF Canonical Format -- 11.5.1. Requirements -- 11.5.2. Format Description -- 11.5.3. Representation of Text Blocks -- 11.5.4. Representation of Vector Graphics -- 11.5.5. Representation of Images -- 11.5.6. Space Coordinates and Device Independence -- 11.6. Document Analysis with XED -- 11.6.1. Parsing PDF Objects -- 11.6.2. Creating the Virtual Document -- 11.6.3. Layout Analysis -- 11.6.4. Evaluation of XED's Physical Structures Extraction -- 11.7. Document Understanding with Dolores -- 11.7.1. Logical Structures and Document Models -- 11.7.2. A First Step Toward Document Reconstruction.

11.7.3. Recovering the Logical Structure from Newspapers.

From 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 Paperlessness", or interesting application areas like "The Role of Document Image Analysis in Trustworthy Elections" as well as "Word Recognition for Museum Index Cards with SNT-Grid". Persons developing document analysis software for industry may also find the chapters useful and attractive. The language of the chapters is simple and clear, along with drawings/diagrams wherever necessary. An adequate number of references are given at the end of each chapter. Overall, the book is highly readable and will be an asset to the community. Renowned contributors include George Nagy, Hiromichi Fujisawa, F Kimura, D Lopresti, Chew Lim Tan, S Uchida, Thierry Paquet, Laurent Heutte, V Govindaraju, R Manmatha.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

There are no comments on this title.

to post a comment.

Powered by Koha