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Table of Contents
Intro
Preface
Contents
Chapter 1: Introduction
1.1 Document Image Processing System
1.2 Structure of a Document
1.3 Categories of Document Layout
1.4 Document Layout Analysis (DLA)
1.5 Why DLA Is Still an Open Area of Research
References
Chapter 2: Document Image Binarization
2.1 Different Types of Degradations and Noise
2.2 Pre-processing
2.3 Document Image Binarization
2.4 Different Binarization Methods
2.4.1 Threshold-Based Methods
2.4.2 Optimization Based Methods
2.4.3 Classification-Based Methods
2.5 Evaluation Techniques
2.5.1 Standard Databases for DIB
2.5.2 Performance Metrics
F-Measure (FM)
Peak Signal-to-Noise Ratio (PSNR)
Distance Reciprocal Distortion (DRD)
Pseudo-F-Measure (PseudoFM)
References
Chapter 3: Document Region Segmentation
3.1 Different Document Region Segmentation Methods
3.1.1 Pixel Analysis-Based Methods
3.1.2 Connected Component Analysis-Based Methods
3.1.3 Local Region Analysis-Based Methods
3.1.4 Hybrid Methods
3.2 Available Dataset for Page Segmentation
3.3 Evaluation Metrices
References
Chapter 4: Document Region Classification
4.1 Different Types of Document Regions
4.2 Different Document Region Classification Methods
4.2.1 Methods for Text/Non-text Classification
Non-machine Learning Based Methods
Shallow Learning Based Methods
Deep Learning Based Methods
4.2.2 Methods for Text Region Classification
4.2.3 Methods for Non-text Classification
Table Detection
Chart Processing
4.3 Evaluation Techniques
4.3.1 Standard Datasets
4.3.2 Evaluation Metrices
References
Chapter 5: Case Study
5.1 Analysis of Basic Contents in Documents (ABCD)
5.1.1 Pre-processing
5.1.2 Non-text Suppression and Noise Removal
5.1.3 Text Region Generation
Region Generation
Region Refinement
5.1.4 Non-text Classification
Identification of Separator and Margin
Identification of Table
5.1.5 Experimental Results
5.2 BINYAS
5.2.1 Pre-processing
5.2.2 Isolation of Separators
5.2.3 Layout Complexity Estimation
5.2.4 Separation of Large and Small Components
5.2.5 Text and Non-text Separation
5.2.6 Thickness Based Text Separation
5.2.7 Text Region Segmentation
5.2.8 Non-text Classification
References
Chapter 6: Summary
Preface
Contents
Chapter 1: Introduction
1.1 Document Image Processing System
1.2 Structure of a Document
1.3 Categories of Document Layout
1.4 Document Layout Analysis (DLA)
1.5 Why DLA Is Still an Open Area of Research
References
Chapter 2: Document Image Binarization
2.1 Different Types of Degradations and Noise
2.2 Pre-processing
2.3 Document Image Binarization
2.4 Different Binarization Methods
2.4.1 Threshold-Based Methods
2.4.2 Optimization Based Methods
2.4.3 Classification-Based Methods
2.5 Evaluation Techniques
2.5.1 Standard Databases for DIB
2.5.2 Performance Metrics
F-Measure (FM)
Peak Signal-to-Noise Ratio (PSNR)
Distance Reciprocal Distortion (DRD)
Pseudo-F-Measure (PseudoFM)
References
Chapter 3: Document Region Segmentation
3.1 Different Document Region Segmentation Methods
3.1.1 Pixel Analysis-Based Methods
3.1.2 Connected Component Analysis-Based Methods
3.1.3 Local Region Analysis-Based Methods
3.1.4 Hybrid Methods
3.2 Available Dataset for Page Segmentation
3.3 Evaluation Metrices
References
Chapter 4: Document Region Classification
4.1 Different Types of Document Regions
4.2 Different Document Region Classification Methods
4.2.1 Methods for Text/Non-text Classification
Non-machine Learning Based Methods
Shallow Learning Based Methods
Deep Learning Based Methods
4.2.2 Methods for Text Region Classification
4.2.3 Methods for Non-text Classification
Table Detection
Chart Processing
4.3 Evaluation Techniques
4.3.1 Standard Datasets
4.3.2 Evaluation Metrices
References
Chapter 5: Case Study
5.1 Analysis of Basic Contents in Documents (ABCD)
5.1.1 Pre-processing
5.1.2 Non-text Suppression and Noise Removal
5.1.3 Text Region Generation
Region Generation
Region Refinement
5.1.4 Non-text Classification
Identification of Separator and Margin
Identification of Table
5.1.5 Experimental Results
5.2 BINYAS
5.2.1 Pre-processing
5.2.2 Isolation of Separators
5.2.3 Layout Complexity Estimation
5.2.4 Separation of Large and Small Components
5.2.5 Text and Non-text Separation
5.2.6 Thickness Based Text Separation
5.2.7 Text Region Segmentation
5.2.8 Non-text Classification
References
Chapter 6: Summary