Buch, Englisch, 434 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 834 g
Buch, Englisch, 434 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 834 g
Reihe: Studies in Computational Intelligence
ISBN: 978-3-540-76279-9
Verlag: Springer
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
- Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computer-Aided Design (CAD)
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
- Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Computeranwendungen in der Mathematik
- Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
Weitere Infos & Material
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.
