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Machine Learning in Document Analysis and Recognition

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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
Attention for Chapter 1: Introduction to Document Analysis and Recognition
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Citations

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25 Dimensions

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108 Mendeley
Summary Dimensions citations
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Chapter title
Introduction to Document Analysis and Recognition
Chapter number 1
Book title
Machine Learning in Document Analysis and Recognition
Published by
Springer, Berlin, Heidelberg, January 2008
DOI 10.1007/978-3-540-76280-5_1
Book ISBNs
978-3-54-076279-9, 978-3-54-076280-5
Authors

Simone Marinai

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 108 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 <1%
France 1 <1%
Norway 1 <1%
Unknown 105 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 30 28%
Student > Ph. D. Student 19 18%
Researcher 11 10%
Student > Bachelor 9 8%
Student > Doctoral Student 5 5%
Other 17 16%
Unknown 17 16%
Readers by discipline Count As %
Computer Science 54 50%
Social Sciences 9 8%
Engineering 9 8%
Arts and Humanities 3 3%
Nursing and Health Professions 2 2%
Other 11 10%
Unknown 20 19%

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