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Recognition of whiteboard notes : online, offline and combination /

"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 meet...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Liwicki, Marcus
Otros Autores: Bunke, Horst
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Singapore ; Hackensack, NJ : World Scientific, ©2008.
Colección:Series in machine perception and artificial intelligence ; v. 71.
Temas:
Acceso en línea:Texto completo

MARC

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100 1 |a Liwicki, Marcus. 
245 1 0 |a Recognition of whiteboard notes :  |b online, offline and combination /  |c Marcus Liwicki, Horst Bunke. 
260 |a Singapore ;  |a Hackensack, NJ :  |b World Scientific,  |c ©2008. 
300 |a 1 online resource (xx, 206 pages) :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a Series in machine perception and artificial intelligence ;  |v v. 71 
504 |a Includes bibliographical references (pages 191-204) and index. 
520 1 |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."--Jacket 
588 0 |a Print version record. 
505 0 |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 0 |a Optical pattern recognition. 
650 0 |a Writing  |x Data processing. 
650 0 |a Interactive whiteboards. 
650 0 |a Image processing  |x Digital techniques. 
650 6 |a Reconnaissance optique des formes (Informatique) 
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650 6 |a Tableaux blancs interactifs. 
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650 7 |a COMPUTERS  |x Optical Data Processing.  |2 bisacsh 
650 7 |a Image processing  |x Digital techniques  |2 fast 
650 7 |a Interactive whiteboards  |2 fast 
650 7 |a Optical pattern recognition  |2 fast 
650 7 |a Writing  |x Data processing  |2 fast 
700 1 |a Bunke, Horst. 
776 0 8 |i Print version:  |a Liwicki, Marcus.  |t Recognition of whiteboard notes.  |d Singapore ; Hackensack, NJ : World Scientific, ©2008  |z 9812814531  |w (DLC) 2009275511  |w (OCoLC)228425513 
830 0 |a Series in machine perception and artificial intelligence ;  |v v. 71. 
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