Machine Learning in Document Analysis and Recognition

ISBN 10: 3540762795 / ISBN 13: 9783540762799

Editore: Springer, 2008

Riguardo questo articolo

pp. 448 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam.

Codice articolo 7542916

Dati bibliografici

• Titolo: Machine Learning in Document Analysis and Recognition
• Editore: Springer
• Data di pubblicazione: 2008
• Lingua: Inglese
• ISBN 10: 3540762795
• ISBN 13: 9783540762799
• Rilegatura: Rilegato
• Condizione: New

Descrizione articolo

Riassunto

The objective of Document Analysis and Recognition (DAR) is to recognize the text and graphical components of a document and to extract information. This book is a collection of research papers and state-of-the-art reviews by leading researchers all over the world. It includes pointers to challenges and opportunities for future research directions. The main goal of the book is to identify good practices for the use of learning strategies in DAR.

Contenuti

to Document Analysis and Recognition.- Structure Extraction in Printed Documents Using Neural Approaches.- Machine Learning for Reading Order Detection in Document Image Understanding.- Decision-Based Specification and Comparison of Table Recognition Algorithms.- Machine Learning for Digital Document Processing: from Layout Analysis to Metadata Extraction.- Classification and Learning Methods for Character Recognition: Advances and Remaining Problems.- Combining Classifiers with Informational Confidence.- Self-Organizing Maps for Clustering in Document Image Analysis.- Adaptive and Interactive Approaches to Document Analysis.- Cursive Character Segmentation Using Neural Network Techniques.- Multiple Hypotheses Document Analysis.- Learning Matching Score Dependencies for Classifier Combination.- Perturbation Models for Generating Synthetic Training Data in Handwriting Recognition.- Review of Classifier Combination Methods.- Machine Learning for Signature Verification.- Off-line Writer Identification and Verification Using Gaussian Mixture Models.