3.4.2 Selection of Essential Keypoints (for Word Spotting)
3.4.3 Spotting Zones
3.4.4 Ranking of Spotted Zone
4 Experimental Results
4.1 Datasets and Ground Truth for Word Spotting
4.2 Datasets and Ground Truth for Figure Spotting
4.3 Determining the Value of
4.4 Selecting the Value of k
4.5 Results and Analysis
5 Conclusion
References
Part II Restoration and Reconstruction of Digital Heritage Artifacts
Text Extraction and Restoration of Old Handwritten Documents
1 Introduction
1.1 Dataset
2 Literature Review
3 Proposed Method

Intro
Preface
Acknowledgments
Organization
Contents
Part I Classification and Retrieval of Heritage Data
Introduction to Heritages and Heritage Management: A Preview
1 Introduction
2 Classification of Heritages
3 Mapping of World Heritages
4 Digital Heritage
5 Public, Classified, and Personal Digital Heritage
6 Science and Digital Heritages
7 Computer-Based Processing of Digital Heritage
8 Threats to Digital Heritage
9 Motivation for Digital Heritage
References
Language-Based Text Categorization: A Survey
1 Introduction

1.1 Monolingual Text Categorization
1.2 Multilingual Text Classification
1.2.1 Automatic Language Identification
1.2.2 Preprocessing
1.2.3 Feature Extraction
1.2.4 Learning/Training Phase
1.3 Language and Document Models
1.4 Applications of Language Identification
1.5 Supervised Learning Methods for Text Categorization
1.5.1 Naïve Bayes Method
1.5.2 K-Nearest Neighbor
1.5.3 Support Vector Machine
1.5.4 Decision Tree Classifier
1.5.5 Neural Networks
1.5.6 Performance Measures
2 Related Work
2.1 Categorization of Monolingual Text Documents

2.2 Multilingual Text Categorization
2.3 Way Forward
3 Conclusion
References
Classification of Yoga Asanas from a Single Image by Learning the 3D View of Human Poses
1 Introduction
2 Related Work
2.1 Multiple-View 3D Human Pose Estimation
2.2 Single-View 3D Human Pose Estimation
3 Proposed Method
3.1 3D Pose Estimation
3.2 Classification of Yoga Asanas Based on Poses
4 Dataset and Experiments
5 Results
6 Observations/Conclusion
References
IHIRD: A Data Set for Indian Heritage Image Retrieval
1 Introduction
2 Description of the Data Set

3 Data Set Preparation
4 Development of Content-Based Image Retrieval System
4.1 Selection of Feature Descriptor and Distance Function
4.2 Indexing of the Image Database
4.3 Ontology-Driven Content-Based Image Search
5 Experimental Results
6 Conclusion
References
Object Spotting in Historical Documents
1 Introduction
2 Related Work
2.1 Word Spotting
2.2 Figure Spotting
3 Proposed Method
3.1 Keypoint Detection
3.2 Keypoint Descriptor
3.3 Estimation of Laplace-Beltrami Operator
3.4 Object Spotting
3.4.1 Candidate Keypoint Selection