Document Processing Using Machine Learning [electronic resource].
Contributor(s): Obaidullah, Sk Md [editor.]
| Santosh, K. C [editor.] | Goncalves, Teresa [editor.] | Das, Nibaran [editor.] | Roy, Kaushik [editor.].
Material type: 
BookPublisher: Milton : CRC Press LLC, 2019Description: 1 online resource (183 p.).ISBN: 9781000739534; 1000739538; 9780429277573; 0429277571; 9781000739831; 100073983X; 9781000739688; 1000739686.Subject(s): COMPUTERS / Computer Graphics / Game Programming & Design | COMPUTERS / Database Management / Data Mining | COMPUTERS / Machine Theory | Image analysis | Document imaging systems | Machine learning | Optical character recognitionDDC classification: 621.3670285631 Online resources: Taylor & Francis | OCLC metadata license agreement Summary: Document Processing Using Machine Learning aims at presenting a handful of resources for students and researchers working in the document image analysis (DIA) domain using machine learning since it covers multiple document processing problems. Starting with an explanation of how Artificial Intelligence (AI) plays an important role in this domain, the book further discusses how different machine learning algorithms can be applied for classification/recognition and clustering problems regardless the type of input data: images or text. In brief, the book offers comprehensive coverage of the most essential topics, including: The role of AI for document image analysis Optical character recognition Machine learning algorithms for document analysis Extreme learning machines and their applications Mathematical foundation for Web text document analysis Social media data analysis Modalities for document dataset generation This book serves both undergraduate and graduate scholars in Computer Science/Information Technology/Electrical and Computer Engineering. Further, it is a great fit for early career research scientists and industrialists in the domain.
Description based upon print version of record.
Document Processing Using Machine Learning aims at presenting a handful of resources for students and researchers working in the document image analysis (DIA) domain using machine learning since it covers multiple document processing problems. Starting with an explanation of how Artificial Intelligence (AI) plays an important role in this domain, the book further discusses how different machine learning algorithms can be applied for classification/recognition and clustering problems regardless the type of input data: images or text. In brief, the book offers comprehensive coverage of the most essential topics, including: The role of AI for document image analysis Optical character recognition Machine learning algorithms for document analysis Extreme learning machines and their applications Mathematical foundation for Web text document analysis Social media data analysis Modalities for document dataset generation This book serves both undergraduate and graduate scholars in Computer Science/Information Technology/Electrical and Computer Engineering. Further, it is a great fit for early career research scientists and industrialists in the domain.
OCLC-licensed vendor bibliographic record.
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