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Machine Learning in Document Analysis and Recognition

Overview of attention for book
Cover of 'Machine Learning in Document Analysis and Recognition'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to Document Analysis and Recognition
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    Chapter 2 Structure Extraction in Printed Documents Using Neural Approaches
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    Chapter 3 Machine Learning for Reading Order Detection in Document Image Understanding
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    Chapter 4 Decision-Based Specification and Comparison of Table Recognition Algorithms
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    Chapter 5 Machine Learning for Digital Document Processing: from Layout Analysis to Metadata Extraction
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    Chapter 6 Classification and Learning Methods for Character Recognition: Advances and Remaining Problems
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    Chapter 7 Combining Classifiers with Informational Confidence
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    Chapter 8 Self-Organizing Maps for Clustering in Document Image Analysis
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    Chapter 9 Adaptive and Interactive Approaches to Document Analysis
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    Chapter 10 Cursive Character Segmentation Using Neural Network Techniques
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    Chapter 11 Multiple Hypotheses Document Analysis
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    Chapter 12 Learning Matching Score Dependencies for Classifier Combination
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    Chapter 13 Perturbation Models for Generating Synthetic Training Data in Handwriting Recognition
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    Chapter 14 Review of Classifier Combination Methods
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    Chapter 15 Machine Learning for Signature Verification
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    Chapter 16 Off-line Writer Identification and Verification Using Gaussian Mixture Models
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Mentioned by

patent
6 patents

Citations

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25 Dimensions

Readers on

mendeley
71 Mendeley
Summary Patents Dimensions citations
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Title
Machine Learning in Document Analysis and Recognition
Published by
Springer Berlin Heidelberg, January 2008
DOI 10.1007/978-3-540-76280-5
ISBNs
978-3-54-076279-9, 978-3-54-076280-5
Editors

Simone Marinai, Hiromichi Fujisawa

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 1%
Italy 1 1%
Unknown 69 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 15 21%
Student > Ph. D. Student 11 15%
Researcher 5 7%
Student > Bachelor 5 7%
Student > Doctoral Student 4 6%
Other 14 20%
Unknown 17 24%
Readers by discipline Count As %
Computer Science 32 45%
Engineering 6 8%
Business, Management and Accounting 2 3%
Social Sciences 2 3%
Economics, Econometrics and Finance 1 1%
Other 6 8%
Unknown 22 31%

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