Handwritten historical document analysis, recognition, and retrieval - state of the art and future trends:
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| Format: | Buch |
| Sprache: | English |
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New Jersey ; London ; Singapore ; Beijing ; Shanghai ; Hong Kong ; Taipei ; Chennai ; Tokyo
World Scientific Publishing Co. Pte. Ltd.
[2021]
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| Schriftenreihe: | Series in machine perception and artificial intelligence
89 |
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| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | 8.2.1 Document Segmentation and Annotation Systems |
| Beschreibung: | xiii, 254 Seiten Illustrationen, Diagramme, Faksimiles |
| ISBN: | 9789811203237 |
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| 245 | 1 | 0 | |a Handwritten historical document analysis, recognition, and retrieval - state of the art and future trends |c Andreas Fischer, Marcus Liwicki, Rolf Ingold |
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| 490 | 0 | |a Series in machine perception and artificial intelligence |v 89 | |
| 500 | |a 8.2.1 Document Segmentation and Annotation Systems | ||
| 505 | 8 | |a Intro -- Contents -- 1. Introduction -- The HisDoc Project -- 2. IAM-HistDB: A Dataset of Handwritten Historical Documents -- 2.1 Introduction -- 2.2 Related Work -- 2.3 The IAM-HistDB -- 2.3.1 Saint Gall Database -- 2.3.2 Parzival Database -- 2.3.3 George Washington Database -- 2.4 Semi-Automatic Ground Truth Creation -- 2.5 Conclusions -- References -- 3. DIVA-HisDB: A Precisely Annotated Dataset of Challenging Medieval Manuscripts -- 3.1 Introduction -- 3.2 Description -- 3.2.1 CSG18 -- 3.2.2 CSG863 -- 3.2.3 CB55 -- 3.3 Creation -- 3.4 Competition -- 3.4.1 Evaluation and Results | |
| 505 | 8 | |a 3.4.1.1 Task-1: Layout Analysis -- 3.4.1.2 Task-2: Baseline Extraction -- 3.4.1.3 Task-3: Line Segmentation -- 3.4.2 Discussion -- References -- 4. Layout Analysis in Handwritten Historical Documents -- 4.1 Introduction -- 4.2 Segmentation in Regions of Interest -- 4.3 Region Description -- 4.4 Typical Processing Steps -- 4.4.1 Binarization -- 4.4.2 Grouping Entities -- 4.4.3 Cutting -- 4.4.4 Labeling Data -- 4.5 Layout Analysis Methods -- 4.5.1 Content Identification -- 4.5.2 Text Line Segmentation -- 4.6 Open Problems -- 4.6.1 Semantical Analysis of the Layout -- 4.6.2 Reading Order | |
| 505 | 8 | |a 4.6.3 Rare Occurrences -- References -- 5. Automatic Handwriting Recognition in Historical Documents -- 5.1 Introduction -- 5.2 Image Preprocessing and Feature Extraction -- 5.3 Character Modeling -- 5.3.1 HMM Character Models -- 5.3.2 LSTM Character Models -- 5.4 Automatic Transcription -- 5.5 Extensions -- 5.6 Conclusions -- References -- 6. Handwritten Keyword Spotting in Historical Documents -- 6.1 Introduction -- 6.2 Related Work -- 6.2.1 Example-Based Search Queries -- 6.2.2 String-Based Search Queries -- 6.2.3 Embedding-Based Search Queries -- 6.3 LSTM NN-Based Keyword Spotting | |
| 505 | 8 | |a 6.3.1 Document Representation -- 6.3.2 LSTM Neural Networks -- 6.3.3 Connectionist Temporal Classification -- 6.3.4 Extending CTC for Efficient Keyword Spotting -- 6.3.5 Experimental Evaluation -- 6.4 Remarks and Further Research -- 6.5 Common Databases -- 6.6 Conclusion -- References -- 7. DIVAServices -- Transforming Document Analysis Methods into Web Services -- 7.1 Abstract -- 7.2 Introduction -- 7.3 Related Work -- 7.3.1 Web Services in Document Image Analysis -- 7.3.2 Web Services in Other Fields -- 7.4 DIVAServices -- The RESTful Web Service Framework | |
| 505 | 8 | |a 7.5 Core Interactions with DivaServices -- 7.5.1 Accessing Method Information -- 7.5.2 Providing Data -- 7.5.3 Execution of a Method -- 7.6 Example Use of DivaServices -- 7.6.1 Upload the Original Image -- 7.6.2 Binarize the Image -- 7.6.3 Extracting Text Lines -- 7.6.4 Performing OCR -- 7.7 The Ecosystem of DivaServices -- 7.7.1 DivaServices-Spotlight -- 7.7.2 DivaServices-WebInterface -- 7.7.3 DivaServices-Management -- 7.8 Conclusion and Future Work -- References -- 8. GraphManuscribble: Interactive Annotation of Historical Manuscripts -- 8.1 Introduction -- 8.2 Related Work | |
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(c) 2021 World Scientific Publishing Company https: //doi.org/10.1142/9789811203244Jmatter Contents 1. Introduction 1 Andreas Fischer, Marcus Liwicki and Rolf Ingold The HisDoc Project 2. 9 IAM-HistDB: A Dataset ofHandwritten Historical Documents 11 Andreas Fischer 2.1 2.2 2.3 3. Introduction. Related Work. The IAM-HistDB. 2.3.1 Saint Gall Database. 2.3.2 Parzival Database. 2.3.3 George Washington Database. 2.4 Semi-Automatic Ground Truth Creation. 2.5 Conclusions. References. 11 12 13 15 15 17 19 21 22 DIVA-HisDB: A Precisely Annotated Dataset of Challenging Medieval Manuscripts 25 Foteini Simistira Liwicki 3.1 3.2 Introduction. Description. 3.2.1 CSG18. 3.2.2 CSG863 . vii 25 26 29 29
Handwritten Historical Document Analysis, Recognition, and Retrieval viii 3.2.3 CB55. Creation. Competition. 3.4.1 Evaluation and Results. 3.4.2 Discussion . References. 30 30 31 34 38 41 Layout Analysis in Handwritten Historical Documents 45 3.3 3.4 4. Mathias Seuret 5. 4.1 4.2 4.3 4.4 Introduction. Segmentation in Regions of Interest. Region Description. TypicalProcessing Steps. 4.4.1 Binarization. 4.4.2 Grouping Entities. 4.4.3 Cutting. 4.4.4 Labeling Data. 4.5 Layout Analysis Methods . 4.5.1 Content Identification. 4.5.2 Text Line Segmentation. 4.6 Open
Problems. 4.6.1 Semantical Analysis of the Layout. 4.6.2 Reading Order. 4.6.3 Rare Occurrences. References. 45 46 48 50 50 52 53 53 54 54 57 62 62 63 63 64 Automatic Handwriting Recognition in Historical Documents 67 Andreas Fischer 5.1 5.2 5.3 Introduction. Image Preprocessingand Feature Extraction. Character Modeling . 5.3.1 HMM Character Models . 5.3.2 LSTM Character Models. 5.4 Automatic Transcription. 5.5 Extensions. 5.6 Conclusions. References. 67 69 70 71 72 74 77 78 79
Contents 6. Handwritten Keyword Spotting in HistoricalDocuments ix 81 Volkmar Frinken and Shriphani Palakodety 6.1 6.2 Introduction. Related Work. 6.2.1 Example-Based Search Queries. 6.2.2 String-Based Search Queries. 6.2.3 Embedding-Based Search Queries. 6.3 LSTM NN-Based Keyword Spotting. 6.3.1 Document Representation. 6.3.2 LSTM Neural Networks. 6.3.3 Connectionist Temporal Classification. 6.3.4 Extending CTC for Efficient Keyword Spotting . . 6.3.5 Experimental Evaluation. 6.4 Remarks and Further Research. 6.5 Common Databases. 6.6 Conclusion. References. 7. DIVAServices - Transforming DocumentAnalysis Methods into Web Services 81 82 83 84 84 85 86 87 88 90 91 93 95 95 97 101 Marcel Gygli 7.1 7.2 7.3 7.4 7.5 7.6 7.7 Abstract. Introduction.
Related Work. 7.3.1 Web Services in Document Image Analysis . 7.3.2 Web Services in Other Fields. DIVAServices - The RESTful Web Service Framework . . Core Interactions with DIVAServices. 7.5.1 Accessing Method Information. 7.5.2 Providing Data. 7.5.3 Execution of a Method. Example Use of DIVAServices. 7.6.1 Upload the Original Image. 7.6.2 Binarize the Image. 7.6.3 Extracting Text Lines. 7.6.4 Performing Optical Character Recognition (OCR) The Ecosystem of DIVAServices. 101 102 103 103 104 104 105 106 107 108 109 110 Ill 112 113 114
Handwritten Historical Document Analysis, Recognition, and Retrieval x 8. 7.7.1 DlVAServices-Spotlight. 7.7.2 DlVAServices-Webinterface. 7.7.3 DlVAServices-Management. 7.8 Conclusion and Future Work . References. 115 116 117 117 118 GraphManuscribble: Interactive Annotation of Historical Manuscripts 121 Angelika Garz 8.1 8.2 Introduction. Related Work. 8.2.1 Document Segmentation and Annotation Systems 8.2.2 Human-Computer Interaction in Image Segmentation. 8.3 Document Graphs . 8.3.1 Basic Definitions for Graphs. 8.3.2 Graph Nodes. 8.3.3 Graph Edges. 8.3.4 Edge Weights. 8.3.5 Graph Clustering . 8.3.6 Split Layout Elements. 8.3.7 Polygonal Graph Representation. 8.3.8 Graph Evaluation. 8.4 Graph-User-
Interaction: Scribbling. 8.4.1 Scribbling as User Interaction Pattern. 8.4.2 User Interaction Evaluation. 8.5 Conclusions and Outlook. References. Related ResearchProjects 9. OldDocPro: OldGreekDocument Recognition 121 125 125 129 132 132 133 136 137 138 139 139 141 144 145 147 148 149 155 157 Basilis Gatos, Georgios Louloudis, Nikolaos Stamatopoulos, George Retsinas, Giorgos Sfikas, Angelos P. Giotis, Foteini Simistira Liwicki, Vassilis Papavassiliou and Vassilis Katsouros 9.1 Introduction 157
Contents xi 9.2 9.3 The GRPOLY-DB Database. 159 Page Segmentation. 162 9.3.1 Performance Evaluation of Page Segmentation . . 162 9.3.2 Word Segmentation. 162 9.3.3 Document Image SegmentationRepresentation . . 164 9.4 Text Recognition. 165 9.4.1 Isolated Character Recognition. 165 9.4.2 Text Line Recognition. 167 9.5 Keyword Spotting . 168 9.6 Conclusions. 171 References. 172 10. Advances in Handwritten Keyword Indexing and Search Technologies 175 Joan Puigcerver, Alejandro H. Toselli and Enrique Vidal 10.1 Introduction. 175 10.2 Proposed Indexing and SearchTechnology. 178 10.2.1 Pixel-Level Word Relevance Probabilities: the “Posteriorgram” . 179 10.2.2 Image Region Word Relevance Probabilities . 180 10.2.3 Minimal Searchable Image Regions: Line-Level KWS. 181 10.2.4 Efficient Computation of Posteriorgrams and Relevance
Probabilities. 181 10.3 Datasets. 182 10.4 Experimental Framework . 185 10.4.1 System Setup. 185 10.4.2 Dataset Usage Detailsand Query Set Selection . . 185 10.4.3 Evaluation Measures. 186 10.5 Laboratory Results. 187 10.6 Demonstration Systems . 188 10.7 Conclusion and Outlook. 189 References. 191 11. Browsing of the Social Network of the Past: Information Extraction from Population Manuscript Images 195 Alicia Fornes, Josep Liados and Joana Maria Pujadas-Mora 11.1 Introduction. 195
xii Handwritten Historical Document Analysis, Recognition, and Retrieval 11.2 Population Records and Datasets. 11.3 System Architecture. 11.4 Image Capture and Document Enhancement. 11.4.1 Layout Analysis and Text Line Extraction . 11.5 Annotation Space. 11.5.1 Key Word Spotting. 11.5.2 Handwritten Text Recognition. 11.6 Semantic Space. 11.6.1 Named Entity Recognition. 11.6.2 Context-Aware Transcription. 11.6.3 Record Linkage . 11.7 User Space. 11.7.1 Crowdsourcing Applications . 11.7.2 The Browsers. 11.8 Conclusions. 11.9 Acknowledgements. References. 12. Lifelong Learning for Text Retrieval and Recognition in Historical Handwritten Document Collections 198 200 202 202 204 204 207 208 208 209 211 211 212 214 215 217 218 221
Lambert Schomaker 12.1 12.2 12.3 12.4 12.5 Introduction. 221 Expectation Management. 226 Deep Learning . 231 The Ball-Park Principle. 232 Technical Realization. 235 12.5.1 Work Flow. 236 12.5.2 Quality and Quantity of Material . 236 12.5.3 Industrialization and Scalability. 237 12.5.4 Human Effort . 237 12.5.5 Algorithms. 237 12.5.6 Object of Recognition: Whole-Word Approaches . 237 12.5.7 Processing Pipeline. 238 12.6 Performance. 240 12.7 Compositionality. 243 12.8 Conclusion. 243 References. 246
Contents 13. Conclusions and Future Trends Andreas Fischer, Marcus Liwicki and Rolf Ingold Index |
| adam_txt |
(c) 2021 World Scientific Publishing Company https: //doi.org/10.1142/9789811203244Jmatter Contents 1. Introduction 1 Andreas Fischer, Marcus Liwicki and Rolf Ingold The HisDoc Project 2. 9 IAM-HistDB: A Dataset ofHandwritten Historical Documents 11 Andreas Fischer 2.1 2.2 2.3 3. Introduction. Related Work. The IAM-HistDB. 2.3.1 Saint Gall Database. 2.3.2 Parzival Database. 2.3.3 George Washington Database. 2.4 Semi-Automatic Ground Truth Creation. 2.5 Conclusions. References. 11 12 13 15 15 17 19 21 22 DIVA-HisDB: A Precisely Annotated Dataset of Challenging Medieval Manuscripts 25 Foteini Simistira Liwicki 3.1 3.2 Introduction. Description. 3.2.1 CSG18. 3.2.2 CSG863 . vii 25 26 29 29
Handwritten Historical Document Analysis, Recognition, and Retrieval viii 3.2.3 CB55. Creation. Competition. 3.4.1 Evaluation and Results. 3.4.2 Discussion . References. 30 30 31 34 38 41 Layout Analysis in Handwritten Historical Documents 45 3.3 3.4 4. Mathias Seuret 5. 4.1 4.2 4.3 4.4 Introduction. Segmentation in Regions of Interest. Region Description. TypicalProcessing Steps. 4.4.1 Binarization. 4.4.2 Grouping Entities. 4.4.3 Cutting. 4.4.4 Labeling Data. 4.5 Layout Analysis Methods . 4.5.1 Content Identification. 4.5.2 Text Line Segmentation. 4.6 Open
Problems. 4.6.1 Semantical Analysis of the Layout. 4.6.2 Reading Order. 4.6.3 Rare Occurrences. References. 45 46 48 50 50 52 53 53 54 54 57 62 62 63 63 64 Automatic Handwriting Recognition in Historical Documents 67 Andreas Fischer 5.1 5.2 5.3 Introduction. Image Preprocessingand Feature Extraction. Character Modeling . 5.3.1 HMM Character Models . 5.3.2 LSTM Character Models. 5.4 Automatic Transcription. 5.5 Extensions. 5.6 Conclusions. References. 67 69 70 71 72 74 77 78 79
Contents 6. Handwritten Keyword Spotting in HistoricalDocuments ix 81 Volkmar Frinken and Shriphani Palakodety 6.1 6.2 Introduction. Related Work. 6.2.1 Example-Based Search Queries. 6.2.2 String-Based Search Queries. 6.2.3 Embedding-Based Search Queries. 6.3 LSTM NN-Based Keyword Spotting. 6.3.1 Document Representation. 6.3.2 LSTM Neural Networks. 6.3.3 Connectionist Temporal Classification. 6.3.4 Extending CTC for Efficient Keyword Spotting . . 6.3.5 Experimental Evaluation. 6.4 Remarks and Further Research. 6.5 Common Databases. 6.6 Conclusion. References. 7. DIVAServices - Transforming DocumentAnalysis Methods into Web Services 81 82 83 84 84 85 86 87 88 90 91 93 95 95 97 101 Marcel Gygli 7.1 7.2 7.3 7.4 7.5 7.6 7.7 Abstract. Introduction.
Related Work. 7.3.1 Web Services in Document Image Analysis . 7.3.2 Web Services in Other Fields. DIVAServices - The RESTful Web Service Framework . . Core Interactions with DIVAServices. 7.5.1 Accessing Method Information. 7.5.2 Providing Data. 7.5.3 Execution of a Method. Example Use of DIVAServices. 7.6.1 Upload the Original Image. 7.6.2 Binarize the Image. 7.6.3 Extracting Text Lines. 7.6.4 Performing Optical Character Recognition (OCR) The Ecosystem of DIVAServices. 101 102 103 103 104 104 105 106 107 108 109 110 Ill 112 113 114
Handwritten Historical Document Analysis, Recognition, and Retrieval x 8. 7.7.1 DlVAServices-Spotlight. 7.7.2 DlVAServices-Webinterface. 7.7.3 DlVAServices-Management. 7.8 Conclusion and Future Work . References. 115 116 117 117 118 GraphManuscribble: Interactive Annotation of Historical Manuscripts 121 Angelika Garz 8.1 8.2 Introduction. Related Work. 8.2.1 Document Segmentation and Annotation Systems 8.2.2 Human-Computer Interaction in Image Segmentation. 8.3 Document Graphs . 8.3.1 Basic Definitions for Graphs. 8.3.2 Graph Nodes. 8.3.3 Graph Edges. 8.3.4 Edge Weights. 8.3.5 Graph Clustering . 8.3.6 Split Layout Elements. 8.3.7 Polygonal Graph Representation. 8.3.8 Graph Evaluation. 8.4 Graph-User-
Interaction: Scribbling. 8.4.1 Scribbling as User Interaction Pattern. 8.4.2 User Interaction Evaluation. 8.5 Conclusions and Outlook. References. Related ResearchProjects 9. OldDocPro: OldGreekDocument Recognition 121 125 125 129 132 132 133 136 137 138 139 139 141 144 145 147 148 149 155 157 Basilis Gatos, Georgios Louloudis, Nikolaos Stamatopoulos, George Retsinas, Giorgos Sfikas, Angelos P. Giotis, Foteini Simistira Liwicki, Vassilis Papavassiliou and Vassilis Katsouros 9.1 Introduction 157
Contents xi 9.2 9.3 The GRPOLY-DB Database. 159 Page Segmentation. 162 9.3.1 Performance Evaluation of Page Segmentation . . 162 9.3.2 Word Segmentation. 162 9.3.3 Document Image SegmentationRepresentation . . 164 9.4 Text Recognition. 165 9.4.1 Isolated Character Recognition. 165 9.4.2 Text Line Recognition. 167 9.5 Keyword Spotting . 168 9.6 Conclusions. 171 References. 172 10. Advances in Handwritten Keyword Indexing and Search Technologies 175 Joan Puigcerver, Alejandro H. Toselli and Enrique Vidal 10.1 Introduction. 175 10.2 Proposed Indexing and SearchTechnology. 178 10.2.1 Pixel-Level Word Relevance Probabilities: the “Posteriorgram” . 179 10.2.2 Image Region Word Relevance Probabilities . 180 10.2.3 Minimal Searchable Image Regions: Line-Level KWS. 181 10.2.4 Efficient Computation of Posteriorgrams and Relevance
Probabilities. 181 10.3 Datasets. 182 10.4 Experimental Framework . 185 10.4.1 System Setup. 185 10.4.2 Dataset Usage Detailsand Query Set Selection . . 185 10.4.3 Evaluation Measures. 186 10.5 Laboratory Results. 187 10.6 Demonstration Systems . 188 10.7 Conclusion and Outlook. 189 References. 191 11. Browsing of the Social Network of the Past: Information Extraction from Population Manuscript Images 195 Alicia Fornes, Josep Liados and Joana Maria Pujadas-Mora 11.1 Introduction. 195
xii Handwritten Historical Document Analysis, Recognition, and Retrieval 11.2 Population Records and Datasets. 11.3 System Architecture. 11.4 Image Capture and Document Enhancement. 11.4.1 Layout Analysis and Text Line Extraction . 11.5 Annotation Space. 11.5.1 Key Word Spotting. 11.5.2 Handwritten Text Recognition. 11.6 Semantic Space. 11.6.1 Named Entity Recognition. 11.6.2 Context-Aware Transcription. 11.6.3 Record Linkage . 11.7 User Space. 11.7.1 Crowdsourcing Applications . 11.7.2 The Browsers. 11.8 Conclusions. 11.9 Acknowledgements. References. 12. Lifelong Learning for Text Retrieval and Recognition in Historical Handwritten Document Collections 198 200 202 202 204 204 207 208 208 209 211 211 212 214 215 217 218 221
Lambert Schomaker 12.1 12.2 12.3 12.4 12.5 Introduction. 221 Expectation Management. 226 Deep Learning . 231 The Ball-Park Principle. 232 Technical Realization. 235 12.5.1 Work Flow. 236 12.5.2 Quality and Quantity of Material . 236 12.5.3 Industrialization and Scalability. 237 12.5.4 Human Effort . 237 12.5.5 Algorithms. 237 12.5.6 Object of Recognition: Whole-Word Approaches . 237 12.5.7 Processing Pipeline. 238 12.6 Performance. 240 12.7 Compositionality. 243 12.8 Conclusion. 243 References. 246
Contents 13. Conclusions and Future Trends Andreas Fischer, Marcus Liwicki and Rolf Ingold Index |
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| contents | Intro -- Contents -- 1. Introduction -- The HisDoc Project -- 2. IAM-HistDB: A Dataset of Handwritten Historical Documents -- 2.1 Introduction -- 2.2 Related Work -- 2.3 The IAM-HistDB -- 2.3.1 Saint Gall Database -- 2.3.2 Parzival Database -- 2.3.3 George Washington Database -- 2.4 Semi-Automatic Ground Truth Creation -- 2.5 Conclusions -- References -- 3. DIVA-HisDB: A Precisely Annotated Dataset of Challenging Medieval Manuscripts -- 3.1 Introduction -- 3.2 Description -- 3.2.1 CSG18 -- 3.2.2 CSG863 -- 3.2.3 CB55 -- 3.3 Creation -- 3.4 Competition -- 3.4.1 Evaluation and Results 3.4.1.1 Task-1: Layout Analysis -- 3.4.1.2 Task-2: Baseline Extraction -- 3.4.1.3 Task-3: Line Segmentation -- 3.4.2 Discussion -- References -- 4. Layout Analysis in Handwritten Historical Documents -- 4.1 Introduction -- 4.2 Segmentation in Regions of Interest -- 4.3 Region Description -- 4.4 Typical Processing Steps -- 4.4.1 Binarization -- 4.4.2 Grouping Entities -- 4.4.3 Cutting -- 4.4.4 Labeling Data -- 4.5 Layout Analysis Methods -- 4.5.1 Content Identification -- 4.5.2 Text Line Segmentation -- 4.6 Open Problems -- 4.6.1 Semantical Analysis of the Layout -- 4.6.2 Reading Order 4.6.3 Rare Occurrences -- References -- 5. Automatic Handwriting Recognition in Historical Documents -- 5.1 Introduction -- 5.2 Image Preprocessing and Feature Extraction -- 5.3 Character Modeling -- 5.3.1 HMM Character Models -- 5.3.2 LSTM Character Models -- 5.4 Automatic Transcription -- 5.5 Extensions -- 5.6 Conclusions -- References -- 6. Handwritten Keyword Spotting in Historical Documents -- 6.1 Introduction -- 6.2 Related Work -- 6.2.1 Example-Based Search Queries -- 6.2.2 String-Based Search Queries -- 6.2.3 Embedding-Based Search Queries -- 6.3 LSTM NN-Based Keyword Spotting 6.3.1 Document Representation -- 6.3.2 LSTM Neural Networks -- 6.3.3 Connectionist Temporal Classification -- 6.3.4 Extending CTC for Efficient Keyword Spotting -- 6.3.5 Experimental Evaluation -- 6.4 Remarks and Further Research -- 6.5 Common Databases -- 6.6 Conclusion -- References -- 7. DIVAServices -- Transforming Document Analysis Methods into Web Services -- 7.1 Abstract -- 7.2 Introduction -- 7.3 Related Work -- 7.3.1 Web Services in Document Image Analysis -- 7.3.2 Web Services in Other Fields -- 7.4 DIVAServices -- The RESTful Web Service Framework 7.5 Core Interactions with DivaServices -- 7.5.1 Accessing Method Information -- 7.5.2 Providing Data -- 7.5.3 Execution of a Method -- 7.6 Example Use of DivaServices -- 7.6.1 Upload the Original Image -- 7.6.2 Binarize the Image -- 7.6.3 Extracting Text Lines -- 7.6.4 Performing OCR -- 7.7 The Ecosystem of DivaServices -- 7.7.1 DivaServices-Spotlight -- 7.7.2 DivaServices-WebInterface -- 7.7.3 DivaServices-Management -- 7.8 Conclusion and Future Work -- References -- 8. GraphManuscribble: Interactive Annotation of Historical Manuscripts -- 8.1 Introduction -- 8.2 Related Work |
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| id | DE-604.BV047106520 |
| illustrated | Illustrated |
| index_date | 2024-07-03T16:25:00Z |
| indexdate | 2024-10-29T05:00:07Z |
| institution | BVB |
| isbn | 9789811203237 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032512779 |
| oclc_num | 1241675840 |
| open_access_boolean | |
| owner | DE-12 |
| owner_facet | DE-12 |
| physical | xiii, 254 Seiten Illustrationen, Diagramme, Faksimiles |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | World Scientific Publishing Co. Pte. Ltd. |
| record_format | marc |
| series2 | Series in machine perception and artificial intelligence |
| spelling | Fischer, Andreas 1947- Verfasser (DE-588)139249516 aut Handwritten historical document analysis, recognition, and retrieval - state of the art and future trends Andreas Fischer, Marcus Liwicki, Rolf Ingold New Jersey ; London ; Singapore ; Beijing ; Shanghai ; Hong Kong ; Taipei ; Chennai ; Tokyo World Scientific Publishing Co. Pte. Ltd. [2021] xiii, 254 Seiten Illustrationen, Diagramme, Faksimiles txt rdacontent n rdamedia nc rdacarrier Series in machine perception and artificial intelligence 89 8.2.1 Document Segmentation and Annotation Systems Intro -- Contents -- 1. Introduction -- The HisDoc Project -- 2. IAM-HistDB: A Dataset of Handwritten Historical Documents -- 2.1 Introduction -- 2.2 Related Work -- 2.3 The IAM-HistDB -- 2.3.1 Saint Gall Database -- 2.3.2 Parzival Database -- 2.3.3 George Washington Database -- 2.4 Semi-Automatic Ground Truth Creation -- 2.5 Conclusions -- References -- 3. DIVA-HisDB: A Precisely Annotated Dataset of Challenging Medieval Manuscripts -- 3.1 Introduction -- 3.2 Description -- 3.2.1 CSG18 -- 3.2.2 CSG863 -- 3.2.3 CB55 -- 3.3 Creation -- 3.4 Competition -- 3.4.1 Evaluation and Results 3.4.1.1 Task-1: Layout Analysis -- 3.4.1.2 Task-2: Baseline Extraction -- 3.4.1.3 Task-3: Line Segmentation -- 3.4.2 Discussion -- References -- 4. Layout Analysis in Handwritten Historical Documents -- 4.1 Introduction -- 4.2 Segmentation in Regions of Interest -- 4.3 Region Description -- 4.4 Typical Processing Steps -- 4.4.1 Binarization -- 4.4.2 Grouping Entities -- 4.4.3 Cutting -- 4.4.4 Labeling Data -- 4.5 Layout Analysis Methods -- 4.5.1 Content Identification -- 4.5.2 Text Line Segmentation -- 4.6 Open Problems -- 4.6.1 Semantical Analysis of the Layout -- 4.6.2 Reading Order 4.6.3 Rare Occurrences -- References -- 5. Automatic Handwriting Recognition in Historical Documents -- 5.1 Introduction -- 5.2 Image Preprocessing and Feature Extraction -- 5.3 Character Modeling -- 5.3.1 HMM Character Models -- 5.3.2 LSTM Character Models -- 5.4 Automatic Transcription -- 5.5 Extensions -- 5.6 Conclusions -- References -- 6. Handwritten Keyword Spotting in Historical Documents -- 6.1 Introduction -- 6.2 Related Work -- 6.2.1 Example-Based Search Queries -- 6.2.2 String-Based Search Queries -- 6.2.3 Embedding-Based Search Queries -- 6.3 LSTM NN-Based Keyword Spotting 6.3.1 Document Representation -- 6.3.2 LSTM Neural Networks -- 6.3.3 Connectionist Temporal Classification -- 6.3.4 Extending CTC for Efficient Keyword Spotting -- 6.3.5 Experimental Evaluation -- 6.4 Remarks and Further Research -- 6.5 Common Databases -- 6.6 Conclusion -- References -- 7. DIVAServices -- Transforming Document Analysis Methods into Web Services -- 7.1 Abstract -- 7.2 Introduction -- 7.3 Related Work -- 7.3.1 Web Services in Document Image Analysis -- 7.3.2 Web Services in Other Fields -- 7.4 DIVAServices -- The RESTful Web Service Framework 7.5 Core Interactions with DivaServices -- 7.5.1 Accessing Method Information -- 7.5.2 Providing Data -- 7.5.3 Execution of a Method -- 7.6 Example Use of DivaServices -- 7.6.1 Upload the Original Image -- 7.6.2 Binarize the Image -- 7.6.3 Extracting Text Lines -- 7.6.4 Performing OCR -- 7.7 The Ecosystem of DivaServices -- 7.7.1 DivaServices-Spotlight -- 7.7.2 DivaServices-WebInterface -- 7.7.3 DivaServices-Management -- 7.8 Conclusion and Future Work -- References -- 8. GraphManuscribble: Interactive Annotation of Historical Manuscripts -- 8.1 Introduction -- 8.2 Related Work Information Retrieval (DE-588)4072803-1 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Handschrift (DE-588)4023287-6 gnd rswk-swf Graphology Archives Electronic books Handschrift (DE-588)4023287-6 s Datenanalyse (DE-588)4123037-1 s Information Retrieval (DE-588)4072803-1 s DE-604 Liwicki, Marcus Sonstige (DE-588)1228552304 oth Ingold, Rolf Sonstige (DE-588)1228552525 oth Erscheint auch als Online-Ausgabe, ebk for institutions 978-981-120-324-4 Erscheint auch als Online-Ausgabe, ebk for individuals 978-981-120-325-1 Digitalisierung BSB München - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032512779&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Fischer, Andreas 1947- Handwritten historical document analysis, recognition, and retrieval - state of the art and future trends Intro -- Contents -- 1. Introduction -- The HisDoc Project -- 2. IAM-HistDB: A Dataset of Handwritten Historical Documents -- 2.1 Introduction -- 2.2 Related Work -- 2.3 The IAM-HistDB -- 2.3.1 Saint Gall Database -- 2.3.2 Parzival Database -- 2.3.3 George Washington Database -- 2.4 Semi-Automatic Ground Truth Creation -- 2.5 Conclusions -- References -- 3. DIVA-HisDB: A Precisely Annotated Dataset of Challenging Medieval Manuscripts -- 3.1 Introduction -- 3.2 Description -- 3.2.1 CSG18 -- 3.2.2 CSG863 -- 3.2.3 CB55 -- 3.3 Creation -- 3.4 Competition -- 3.4.1 Evaluation and Results 3.4.1.1 Task-1: Layout Analysis -- 3.4.1.2 Task-2: Baseline Extraction -- 3.4.1.3 Task-3: Line Segmentation -- 3.4.2 Discussion -- References -- 4. Layout Analysis in Handwritten Historical Documents -- 4.1 Introduction -- 4.2 Segmentation in Regions of Interest -- 4.3 Region Description -- 4.4 Typical Processing Steps -- 4.4.1 Binarization -- 4.4.2 Grouping Entities -- 4.4.3 Cutting -- 4.4.4 Labeling Data -- 4.5 Layout Analysis Methods -- 4.5.1 Content Identification -- 4.5.2 Text Line Segmentation -- 4.6 Open Problems -- 4.6.1 Semantical Analysis of the Layout -- 4.6.2 Reading Order 4.6.3 Rare Occurrences -- References -- 5. Automatic Handwriting Recognition in Historical Documents -- 5.1 Introduction -- 5.2 Image Preprocessing and Feature Extraction -- 5.3 Character Modeling -- 5.3.1 HMM Character Models -- 5.3.2 LSTM Character Models -- 5.4 Automatic Transcription -- 5.5 Extensions -- 5.6 Conclusions -- References -- 6. Handwritten Keyword Spotting in Historical Documents -- 6.1 Introduction -- 6.2 Related Work -- 6.2.1 Example-Based Search Queries -- 6.2.2 String-Based Search Queries -- 6.2.3 Embedding-Based Search Queries -- 6.3 LSTM NN-Based Keyword Spotting 6.3.1 Document Representation -- 6.3.2 LSTM Neural Networks -- 6.3.3 Connectionist Temporal Classification -- 6.3.4 Extending CTC for Efficient Keyword Spotting -- 6.3.5 Experimental Evaluation -- 6.4 Remarks and Further Research -- 6.5 Common Databases -- 6.6 Conclusion -- References -- 7. DIVAServices -- Transforming Document Analysis Methods into Web Services -- 7.1 Abstract -- 7.2 Introduction -- 7.3 Related Work -- 7.3.1 Web Services in Document Image Analysis -- 7.3.2 Web Services in Other Fields -- 7.4 DIVAServices -- The RESTful Web Service Framework 7.5 Core Interactions with DivaServices -- 7.5.1 Accessing Method Information -- 7.5.2 Providing Data -- 7.5.3 Execution of a Method -- 7.6 Example Use of DivaServices -- 7.6.1 Upload the Original Image -- 7.6.2 Binarize the Image -- 7.6.3 Extracting Text Lines -- 7.6.4 Performing OCR -- 7.7 The Ecosystem of DivaServices -- 7.7.1 DivaServices-Spotlight -- 7.7.2 DivaServices-WebInterface -- 7.7.3 DivaServices-Management -- 7.8 Conclusion and Future Work -- References -- 8. GraphManuscribble: Interactive Annotation of Historical Manuscripts -- 8.1 Introduction -- 8.2 Related Work Information Retrieval (DE-588)4072803-1 gnd Datenanalyse (DE-588)4123037-1 gnd Handschrift (DE-588)4023287-6 gnd |
| subject_GND | (DE-588)4072803-1 (DE-588)4123037-1 (DE-588)4023287-6 |
| title | Handwritten historical document analysis, recognition, and retrieval - state of the art and future trends |
| title_auth | Handwritten historical document analysis, recognition, and retrieval - state of the art and future trends |
| title_exact_search | Handwritten historical document analysis, recognition, and retrieval - state of the art and future trends |
| title_exact_search_txtP | Handwritten historical document analysis, recognition, and retrieval - state of the art and future trends |
| title_full | Handwritten historical document analysis, recognition, and retrieval - state of the art and future trends Andreas Fischer, Marcus Liwicki, Rolf Ingold |
| title_fullStr | Handwritten historical document analysis, recognition, and retrieval - state of the art and future trends Andreas Fischer, Marcus Liwicki, Rolf Ingold |
| title_full_unstemmed | Handwritten historical document analysis, recognition, and retrieval - state of the art and future trends Andreas Fischer, Marcus Liwicki, Rolf Ingold |
| title_short | Handwritten historical document analysis, recognition, and retrieval - state of the art and future trends |
| title_sort | handwritten historical document analysis recognition and retrieval state of the art and future trends |
| topic | Information Retrieval (DE-588)4072803-1 gnd Datenanalyse (DE-588)4123037-1 gnd Handschrift (DE-588)4023287-6 gnd |
| topic_facet | Information Retrieval Datenanalyse Handschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032512779&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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