Hidden Markov Models for Handwriting Recognition

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Table of contents

List of Tables
List of Figures
Introduction
I HANDWRITING RECOGNITION — OVERVIEW
1 Offline Handwriting Recognition – Overview of the Problem
1.1 Introduction
1.2 Preliminary Steps to Offline Handwriting Recognition
1.3 Reducing Handwriting Variability with Image Processing Techniques .
1.3.1 Normalizing Contrast
1.3.2 Normalizing Skew
1.3.3 Normalizing Slant
1.3.4 Normalizing Size
1.4 Extraction of Relevant Features for Handwriting Recognition
1.4.1 Text Segmentation for Feature Extraction
1.4.2 Features for Handwriting Representation
1.5 Modeling Handwriting
1.5.1 Whole-Word Models
1.5.2 Part-Based Methods
1.5.3 Segmentation-Free Approach
1.6 Modeling the Language to Constrain and Improve the Recognition
1.6.1 Vocabulary
1.6.2 Language Modeling
1.6.3 Open-Vocabulary Approaches
1.7 Measuring the Quality of the Recognition
1.8 Conclusion
2 Handwriting Recognition with Hidden Markov Models and Neural Networks
2.1 Introduction
2.2 Hidden Markov Models for Handwriting Recognition
2.2.1 Definition
2.2.2 Choice of Topology
2.2.3 Choice of Emission Distribution
2.2.4 Model Refinements
2.2.5 Decoding
2.3 Neural Networks for Handwriting Recognition
2.3.1 The Multi-Layer Perceptron
2.3.2 Recurrent Neural Networks
2.3.3 Long Short-Term Memory Units
2.3.4 Convolutional Neural Networks
2.4 Handwriting Recognition Systems with Neural Networks
2.4.1 The Hybrid NN/HMM scheme
2.4.2 Predicting Characters
2.4.3 NN Feature Extractors
2.5 Training Models
2.5.1 Training Hidden Markov Models with Generative Emission Models
2.5.2 Training Neural Networks
2.5.3 Training Deep Neural Networks
2.5.4 Training Complete Handwriting Recognition Systems
2.6 Conclusion
II EXPERIMENTAL SETUP
3 Databases and Software
3.1 Introduction
3.2 Databases of Handwritten Text
3.2.1 Rimes
3.2.2 IAM
3.2.3 Bentham
3.3 Software
3.4 A Note about the Experimental Setup in the Next Chapters
4 Baseline System
4.1 Introduction
4.2 Preprocessing and Feature Extraction
4.2.1 Image Preprocessing
4.2.2 Feature Extraction with Sliding Windows
4.3 Language Models
4.3.1 Corpus Preparation and Vocabulary Selection
4.3.2 Language Models Estimation
4.3.3 Recognition Output Normalization
4.4 Decoding Method
4.5 A GMM/HMM baseline system
4.5.1 HMM topology selection
4.5.2 GMM/HMM training
4.5.3 Results
4.6 Conclusion
III DEEP NEURAL NETWORKS IN HIDDEN MARKOV MODEL SYSYEMS
5 Hybrid Deep Multi-Layer Perceptrons /HMMfor Handwriting Recognition
5.1 Introduction
5.2 Experimental Setup
5.3 Study of the Influence of Input Context
5.3.1 Alignments from GMM/HMM Systems
5.3.2 Handcrafted Features
5.3.3 Pixel Intensities
5.4 Study of the Impact of Depth in MLPs
5.4.1 Deep MLPs
5.4.2 Deep vs Wide MLPs
5.5 Study of the Benefits of Sequence-Discriminative Training
5.6 Study of the Choice of Inputs
5.7 Conclusion
6 Hybrid Deep Recurrent Neural Networks / HMM for Handwriting Recognition
6.1 Introduction
6.2 Experimental Setup
6.2.1 RNN Architecture Overview
6.2.2 Decoding in the Hybrid NN/HMM Framework
6.3 Study of the Influence of Input Context
6.3.1 Including Context with Frame Concatenation
6.3.2 Context through the Recurrent Connections
6.4 Study of the Influence of Recurrence
6.5 Study of the Impact of Depth in BLSTM-RNNs
6.5.1 Deep BLSTM-RNNs
6.5.2 Deep vs Wide BLSTM-RNNs
6.5.3 Analysis
6.6 Study of the Impact of Dropout
6.6.1 Dropout after the Recurrent Layers
6.6.2 Dropout at Different Positions
6.6.3 Study of the Effect of Dropout in Complete Systems (with LM)
6.7 Study of the Choice of Inputs
6.8 Conclusion
IV COMPARISON AND COMBINATION OF DEEP MLPs AND RNNs
7 Experimental Comparison of Framewise and CTC Training
7.1 Introduction
7.2 Experimental Setup
14 Contents
7.3 Relation between CTC and Forward-Backward Training of Hybrid NN/ HMMs
7.3.1 Notations
7.3.2 The Equations of Forward-Backward Training of Hybrid NN/ HMMs
7.3.3 The Equations of CTC Training of RNNs
7.3.4 Similarities between CTC and hybrid NN/HMM Training
7.4 Topology and Blank
7.5 CTC Training of MLPs
7.6 Framewise vs CTC Training
7.7 Interaction between CTC Training and the Blank Symbol
7.7.1 Peaks
7.7.2 Trying to avoid the Peaks of Predictions
7.7.3 The advantages of prediction peaks
7.8 CTC Training without Blanks
7.9 The Role of the Blank Symbol
7.10 Conclusion
8 Experimental Results, Combinations and Discussion
8.1 Introduction
8.2 Summary of Results on Rimes and IAM Databases
8.2.1 MLP/HMM Results
8.2.2 RNN/HMM Results
8.2.3 Comparison of MLP/HMM and RNN/HMM Results
8.2.4 Combination of the Proposed Systems
8.3 The Handwritten Text Recognition tranScriptorium (HTRtS) Challenge
8.3.1 Presentation of the HTRtS Evaluation and of the Experimental Setup
8.3.2 Systems Submitted to the Restricted Track
8.3.3 Systems Submitted to the Unrestricted Track
8.3.4 Post-Evaluation Improvements
8.4 Conclusion
Conclusions and Perspectives
List of Publications
Appendices
A Databases
A.1 IAM
A.2 Rimes (ICDAR 2011 setup)
A.3 Bentham (HTRtS 2014 setup)
B Résumé Long
B.1 Système de Base
B.2 Systèmes Hybrides Perceptrons Multi-Couches Profonds / MMC
B.3 Systèmes Hybrides Réseaux de Neurones Récurrents Profonds / MMC
B.4 Une Comparaison Expérimentale de l’Entrai-nement CTC et au Niveau Trame
B.5 Combinaisons et Résultats Finaux
B.6 Conclusions et Perspectives
Bibliography