Recognition of whiteboard notes: online, offline and combination
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore
World Scientific
c2008
|
| Schriftenreihe: | Series in machine perception and artificial intelligence
v. 71 |
| Schlagworte: | |
| Online-Zugang: | FAW01 FAW02 Volltext |
| Beschreibung: | Includes bibliographical references (p. 191-204) and index "This book addresses the task of processing online handwritten notes acquired from an electronic whiteboard, which is a new modality in handwriting recognition research. The main motivation of this book is smart meeting rooms, aim to automate standard tasks usually performed by humans in a meeting." "The book can be summarized as follows. A new online handwritten database is compiled, and four handwriting recognition systems are developed. Moreover, novel preprocessing and normalization strategies are designed especially for whiteboard notes and a new neural network based recognizer is applied. Commercial recognition systems are included in a multiple classifier system."--BOOK JACKET. 1. Introduction. 1.1. Motivation. 1.2. Handwriting recognition. 1.3. Comparability of recognition results. 1.4. Related topics. 1.5. Contribution. 1.6. Ouline -- 2. Classification methods. 2.1. Hidden Markov models. 2.2. Neural networks. 2.3. Gaussian mixture models. 2.4. Language models -- 3. Linguistic resources and handwriting databases. 3.1. Linguistic resources. 3.2. IAM offline database. 3.3. IAM online database -- 4. Offline approach. 4.1. System description. 4.2. Enhancing the training set. 4.3. Experiments. 4.4. Word extraction. 4.5. Conclusions -- 5. Online approach. 5.1. Line segmentation. 5.2. Preprocessing. 5.3. Features. 5.4. HMM-based experiments. 5.5. Experiments with neural networks. 5.6. Conclusions and discussion -- 6. Multiple classifier combination. 6.1. Methodology. 6.2. Recognition systems. 6.3. Initial experiments. 6.4. Experiments with all recognition systems. 6.5. Advanced confidence measures. 6.6. Conclusions -- 7. Writer-dependent recognition. 7.1. Writer identification. 7.2. Writer-dependent experiments. 7.3. Automatic handwriting classification. 7.4. Conclusions -- 8. Conclusions. 8.1. Overview of recognition systems. 8.2. Overview of experimental results. 8.3. Concluding remarks. 8.4. Outlook |
| Beschreibung: | 1 Online-Ressource (xx, 206 p.) |
| ISBN: | 9789812814531 9789812814548 9812814531 981281454X |
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| 245 | 1 | 0 | |a Recognition of whiteboard notes |b online, offline and combination |c Marcus Liwicki, Horst Bunke |
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| 490 | 0 | |a Series in machine perception and artificial intelligence |v v. 71 | |
| 500 | |a Includes bibliographical references (p. 191-204) and index | ||
| 500 | |a "This book addresses the task of processing online handwritten notes acquired from an electronic whiteboard, which is a new modality in handwriting recognition research. The main motivation of this book is smart meeting rooms, aim to automate standard tasks usually performed by humans in a meeting." "The book can be summarized as follows. A new online handwritten database is compiled, and four handwriting recognition systems are developed. Moreover, novel preprocessing and normalization strategies are designed especially for whiteboard notes and a new neural network based recognizer is applied. Commercial recognition systems are included in a multiple classifier system."--BOOK JACKET. | ||
| 500 | |a 1. Introduction. 1.1. Motivation. 1.2. Handwriting recognition. 1.3. Comparability of recognition results. 1.4. Related topics. 1.5. Contribution. 1.6. Ouline -- 2. Classification methods. 2.1. Hidden Markov models. 2.2. Neural networks. 2.3. Gaussian mixture models. 2.4. Language models -- 3. Linguistic resources and handwriting databases. 3.1. Linguistic resources. 3.2. IAM offline database. 3.3. IAM online database -- 4. Offline approach. 4.1. System description. 4.2. Enhancing the training set. 4.3. Experiments. 4.4. Word extraction. 4.5. Conclusions -- 5. Online approach. 5.1. Line segmentation. 5.2. Preprocessing. 5.3. Features. 5.4. HMM-based experiments. 5.5. Experiments with neural networks. 5.6. Conclusions and discussion -- 6. Multiple classifier combination. 6.1. Methodology. 6.2. Recognition systems. 6.3. Initial experiments. 6.4. Experiments with all recognition systems. 6.5. Advanced confidence measures. 6.6. Conclusions -- 7. Writer-dependent recognition. 7.1. Writer identification. 7.2. Writer-dependent experiments. 7.3. Automatic handwriting classification. 7.4. Conclusions -- 8. Conclusions. 8.1. Overview of recognition systems. 8.2. Overview of experimental results. 8.3. Concluding remarks. 8.4. Outlook | ||
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| 650 | 7 | |a Writing / Data processing |2 fast | |
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| id | DE-604.BV043124752 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:18:11Z |
| institution | BVB |
| isbn | 9789812814531 9789812814548 9812814531 981281454X |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028548942 |
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| physical | 1 Online-Ressource (xx, 206 p.) |
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| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | World Scientific |
| record_format | marc |
| series2 | Series in machine perception and artificial intelligence |
| spelling | Liwicki, Marcus Verfasser aut Recognition of whiteboard notes online, offline and combination Marcus Liwicki, Horst Bunke Singapore World Scientific c2008 1 Online-Ressource (xx, 206 p.) txt rdacontent c rdamedia cr rdacarrier Series in machine perception and artificial intelligence v. 71 Includes bibliographical references (p. 191-204) and index "This book addresses the task of processing online handwritten notes acquired from an electronic whiteboard, which is a new modality in handwriting recognition research. The main motivation of this book is smart meeting rooms, aim to automate standard tasks usually performed by humans in a meeting." "The book can be summarized as follows. A new online handwritten database is compiled, and four handwriting recognition systems are developed. Moreover, novel preprocessing and normalization strategies are designed especially for whiteboard notes and a new neural network based recognizer is applied. Commercial recognition systems are included in a multiple classifier system."--BOOK JACKET. 1. Introduction. 1.1. Motivation. 1.2. Handwriting recognition. 1.3. Comparability of recognition results. 1.4. Related topics. 1.5. Contribution. 1.6. Ouline -- 2. Classification methods. 2.1. Hidden Markov models. 2.2. Neural networks. 2.3. Gaussian mixture models. 2.4. Language models -- 3. Linguistic resources and handwriting databases. 3.1. Linguistic resources. 3.2. IAM offline database. 3.3. IAM online database -- 4. Offline approach. 4.1. System description. 4.2. Enhancing the training set. 4.3. Experiments. 4.4. Word extraction. 4.5. Conclusions -- 5. Online approach. 5.1. Line segmentation. 5.2. Preprocessing. 5.3. Features. 5.4. HMM-based experiments. 5.5. Experiments with neural networks. 5.6. Conclusions and discussion -- 6. Multiple classifier combination. 6.1. Methodology. 6.2. Recognition systems. 6.3. Initial experiments. 6.4. Experiments with all recognition systems. 6.5. Advanced confidence measures. 6.6. Conclusions -- 7. Writer-dependent recognition. 7.1. Writer identification. 7.2. Writer-dependent experiments. 7.3. Automatic handwriting classification. 7.4. Conclusions -- 8. Conclusions. 8.1. Overview of recognition systems. 8.2. Overview of experimental results. 8.3. Concluding remarks. 8.4. Outlook COMPUTERS / Computer Vision & Pattern Recognition bisacsh COMPUTERS / Optical Data Processing bisacsh Image processing / Digital techniques fast Interactive whiteboards fast Optical pattern recognition fast Writing / Data processing fast Datenverarbeitung Optical pattern recognition Writing Data processing Interactive whiteboards Image processing Digital techniques Bunke, Horst Sonstige oth http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=521164 Aggregator Volltext |
| spellingShingle | Liwicki, Marcus Recognition of whiteboard notes online, offline and combination COMPUTERS / Computer Vision & Pattern Recognition bisacsh COMPUTERS / Optical Data Processing bisacsh Image processing / Digital techniques fast Interactive whiteboards fast Optical pattern recognition fast Writing / Data processing fast Datenverarbeitung Optical pattern recognition Writing Data processing Interactive whiteboards Image processing Digital techniques |
| title | Recognition of whiteboard notes online, offline and combination |
| title_auth | Recognition of whiteboard notes online, offline and combination |
| title_exact_search | Recognition of whiteboard notes online, offline and combination |
| title_full | Recognition of whiteboard notes online, offline and combination Marcus Liwicki, Horst Bunke |
| title_fullStr | Recognition of whiteboard notes online, offline and combination Marcus Liwicki, Horst Bunke |
| title_full_unstemmed | Recognition of whiteboard notes online, offline and combination Marcus Liwicki, Horst Bunke |
| title_short | Recognition of whiteboard notes |
| title_sort | recognition of whiteboard notes online offline and combination |
| title_sub | online, offline and combination |
| topic | COMPUTERS / Computer Vision & Pattern Recognition bisacsh COMPUTERS / Optical Data Processing bisacsh Image processing / Digital techniques fast Interactive whiteboards fast Optical pattern recognition fast Writing / Data processing fast Datenverarbeitung Optical pattern recognition Writing Data processing Interactive whiteboards Image processing Digital techniques |
| topic_facet | COMPUTERS / Computer Vision & Pattern Recognition COMPUTERS / Optical Data Processing Image processing / Digital techniques Interactive whiteboards Optical pattern recognition Writing / Data processing Datenverarbeitung Writing Data processing Image processing Digital techniques |
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