Document Analysis and Recognition – ICDAR 2021: 16th International Conference, Lausanne, Switzerland, September 5–10, 2021, Proceedings, Part II (Lecture Notes in Computer Science, 12822) [1st ed. 2021] 3030863301, 9783030863302

Table of contents :
Foreword
Preface
Organization
Contents – Part II
Document Analysis for Literature Search
Towards Document Panoptic Segmentation with Pinpoint Accuracy: Method and Evaluation
1 Introduction
2 Related Work
3 Document Panoptic Segmentation Model
3.1 System Overview
3.2 Column Region Segmentation
3.3 Line Region Splitting
3.4 Line Region Sequence Labeling
3.5 Page Representation
4 Evaluation Metric
5 Experiments and Results
5.1 Dataset
5.2 Baseline Methods and Proposed Method
5.3 Experimental Results
5.4 Application
6 Conclusion
References
A Math Formula Extraction and Evaluation Framework for PDF Documents
1 Introduction
2 Related Work
3 Formula Detection Pipeline Components
3.1 SymbolScraper: Symbol Extraction from PDF
3.2 Formula Detection in Page Images
3.3 Recognizing Formula Structure (Parsing)
4 Results for Pipeline Components
5 New Tools for Visualization and Evaluation
6 Conclusion
References
Toward Automatic Interpretation of 3D Plots
1 Introduction
2 Related Work
3 Method
3.1 Network Architecture
3.2 The SurfaceGrid Dataset
3.3 Training Details
4 Experiments
4.1 Final Model Performance
4.2 Impact of Training Set Components on Performance
4.3 Performance on Real Plots and Wireframe Models
5 Conclusion
References
Document Summarization and Translation
Can Text Summarization Enhance the Headline Stance Detection Task? Benefits and Drawbacks
1 Introduction
2 Related Work
3 Text Summarization Approaches
3.1 Extractive Summarization
3.2 Abstractive Summarization
3.3 Hybrid Summarization
4 Stance Detection Approaches
4.1 Deep Learning Stance Detection Approach
4.2 Machine Learning Stance Detection Approach
5 Evaluation Environment
5.1 Datasets
5.2 Experiments
5.3 Evaluation Metrics and Results
5.4 Discussion
6 Conclusion and Future Work
References
The Biased Coin Flip Process for Nonparametric Topic Modeling
1 Introduction
2 Background
2.1 Dirichlet Process
2.2 Latent Dirichlet Allocation
2.3 Hierarchical Dirichlet Processes
3 Methods
3.1 Hierarchical Biased Coin Flip Process
4 Results
4.1 Perplexity
4.2 K-Finding
4.3 Gaussian Mixture Models
5 Conclusion
References
CoMSum and SIBERT: A Dataset and Neural Model for Query-Based Multi-document Summarization
1 Introduction
2 CoMSum: Automatically Generated Summarization Dataset
2.1 Overview
2.2 Creating Contextual Multi-document Summarization (CoMSum)
2.3 Quality Evaluation of CoMSum
3 SIBERT: A Use of CoMSum for Extractive Summarization
3.1 The SIBERT Model
3.2 Experiments
4 Related Work
5 Conclusion
References
RTNet: An End-to-End Method for Handwritten Text Image Translation
1 Introduction
2 Related Works
2.1 Machine Translation
2.2 Text Line Recognition
2.3 Handwritten Text Line Translation
3 Methodology
3.1 Architecture
3.2 Theory
3.3 Training Strategy
3.4 OCR Model
3.5 MT Model
3.6 Autoregressive Decoding
4 Experiments
4.1 Dataset
4.2 Pre-training Results
4.3 End-to-End Results
5 Conclusions
References
Multimedia Document Analysis
NTable: A Dataset for Camera-Based Table Detection
1 Introduction
2 Related Work
2.1 Table Detection Dataset
2.2 Table Detection
3 The NTable Dataset
3.1 NTable-ori and NTable-cam
3.2 NTable-gen
3.3 Manually and Automatic Annotation
4 Experiment
4.1 Data and Metrics
4.2 Experimental Results and Analysis
5 Conclusion
References
Label Selection Algorithm Based on Boolean Interpolative Decomposition with Sequential Backward Selection for Multi-label Classification
1 Introduction
2 Preliminaries
3 A Label Selection Algorithm Based on Boolean Interpolative Decomposition with Sequential Backward Selection
3.1 Uninformative Label Reduction via Exact Boolean Matrix Decomposition
3.2 Less Informative Label Reduction via Sequential Backward Selection
3.3 Label Selection Algorithm Based on BID with SBS
4 Experiments
4.1 Six Benchmark Data Sets and Two Evaluation Metrics
4.2 Experimental Settings
4.3 Reconstruction Error Analysis for Three BMD-Type Methods
4.4 Classification Performance Comparison and Analysis
5 Conclusions
References
GSSF: A Generative Sequence Similarity Function Based on a Seq2Seq Model for Clustering Online Handwritten Mathematical Answers
1 Introduction
2 Related Works
2.1 Computer-Assisted Marking
2.2 Seq2seq OnHME Recognizer
3 Proposed Method
3.1 Generative Sequence Similarity Function
3.2 Similarity-Based Representation
4 Clustering-Based Measurement
5 Experiments
5.1 Datasets
5.2 OnHME Recognizer
5.3 Experimental Settings
5.4 Evaluation
5.5 Visualizing Similarity-Based Representation Matrix
6 Conclusion and Future Works
References
C2VNet: A Deep Learning Framework Towards Comic Strip to Audio-Visual Scene Synthesis
1 Introduction
2 IMCDB: Proposed Indian Mythological Comic Dataset
3 C2VNet Architecture
3.1 Preprocessing
3.2 Extraction of Comic Elements
3.3 Video Asset Creation
3.4 Video Composition
4 Experiments and Results
4.1 Training and Testing
4.2 Results
5 Conclusion
References
LSTMVAEF: Vivid Layout via LSTM-Based Variational Autoencoder Framework
1 Introduction
2 Related Work
3 Framework
3.1 LSTM-Based VAE Framework
3.2 Loss Function Design
4 Experiments
4.1 Details for Generation
4.2 Layout Generation Results
4.3 Pre-training for Document Layout Analysis
5 Conclusions
References
Mobile Text Recognition
HCRNN: A Novel Architecture for Fast Online Handwritten Stroke Classification
1 Introduction
2 Related Work
3 HDNN for Handwritten Stroke Classification
3.1 Background
3.2 HCRNN Architecture
4 Fragment Pooling
4.1 Fragment Max Pooling
4.2 Fragment Average Pooling
4.3 Fragment Stochastic Pooling
4.4 Mixed Fragment Pooling
5 Experimental Results
5.1 Dataset Description
5.2 Evaluation Procedure
5.3 Ablation Study
6 Conclusion and Outlook
References
RFDoc: Memory Efficient Local Descriptors for ID Documents Localization and Classification
1 Introduction
2 Previous Work
3 A Dataset of Aligned Patches from ID Document Images
4 Proposed Image Descriptor
4.1 Image Patch Descriptor Construction
4.2 Training and Features Selection
5 Experimental Evaluation
5.1 Training and Features Selection
5.2 Performance in ID Document Patches Verification
5.3 Complex Camera-Based Document Recognition
5.4 Memory Consumption
5.5 Matching Speed Comparison
5.6 Conclusion
References
Dynamic Receptive Field Adaptation for Attention-Based Text Recognition
1 Introduction
2 Related Work
2.1 Segmentation-Based Approaches
2.2 Sequence-Based Approaches
3 Method
3.1 Feature Encoder
3.2 Recurrent Attention Decoder
3.3 Dynamic Feature Adaption
3.4 Memory Attention
4 Experiments
4.1 Datasets
4.2 Implementation Details
4.3 Ablation Study
4.4 Comparisons with State-of-the-Arts
4.5 Visual Illustrations
5 Conclusion
References
Context-Free TextSpotter for Real-Time and Mobile End-to-End Text Detection and Recognition
1 Introduction
2 Related Work
2.1 Text Detection and Recognition
2.2 End-to-End Text Recognition
2.3 Real-Time Image Recognition
3 Method
3.1 Text-Box Detector and Character Spotter
3.2 Character Decoder
3.3 Feature Extractor
3.4 Training
4 Experiments
4.1 Results
4.2 On-Device Benchmarking
5 Conclusion
References
MIDV-LAIT: A Challenging Dataset for Recognition of IDs with Perso-Arabic, Thai, and Indian Scripts
1 Introduction
2 Datasets for the Indian, Thai, and Perso-Arabic Scripts
3 MIDV-LAIT Dataset
3.1 MIDV-LAIT Target Problems
4 Baseline
4.1 Tesseract OCR 4.1.0
4.2 Experimental Results
5 Conclusion and Future Work
References
Determining Optimal Frame Processing Strategies for Real-Time Document Recognition Systems
1 Introduction
2 Framework
2.1 Real-Time Video Frames Recognition Model
2.2 Frame Processing Strategies
3 Experimental Evaluation
3.1 Dataset
3.2 Frame Processing Time
3.3 Comparing Combination Strategies
3.4 Performance Profiles with Stopping Rules
4 Discussion
References
Document Analysis for Social Good
Embedded Attributes for Cuneiform Sign Spotting
1 Introduction
2 Related Work
2.1 Cuneiform Character Retrieval
2.2 Word Embeddings
3 Cuneiform Sign Expressions
3.1 Gottstein System
3.2 Gottstein with Spatial Information
4 Evaluation
4.1 Database
4.2 Model Architecture
4.3 Training Setup
4.4 Similarity Metric
4.5 Evaluation Protocol
4.6 Evaluation Metrics
4.7 Results and Discussion
5 Conclusion
References
Date Estimation in the Wild of Scanned Historical Photos: An Image Retrieval Approach
1 Introduction
2 Related Work
3 Learning Objectives
4 Training Process
5 Experiments
6 Conclusions
References
Two-Step Fine-Tuned Convolutional Neural Networks for Multi-label Classification of Children's Drawings
1 Introduction
2 Related Work
3 Methodology
3.1 Data Preparation
3.2 Two-Step Fine Tuning
3.3 Multi-label Classification
4 Experimental Protocol, Results and Analysis
5 Conclusion
References
DCINN: Deformable Convolution and Inception Based Neural Network for Tattoo Text Detection Through Skin Region
1 Introduction
2 Related Work
3 Proposed Approach
3.1 Skin Region Detection Using YCrCb and HSV Color Space
3.2 Deep Model for Tattoo Text Detection from Skin Region
4 Experimental Results
4.1 Ablation Study
4.2 Experiments for Tattoo Text Detection
4.3 Experiments on Natural Scene Text Dataset
5 Conclusion and Future Work
References
Sparse Document Analysis Using Beta-Liouville Naive Bayes with Vocabulary Knowledge
1 Introduction
2 The Dirichlet Smoothing for Multinomial Bayesian Inference
3 The Liouville Assumption Using Vocabulary Knowledge
4 Experimental Results
4.1 Emotion Intensity Analysis
4.2 Hate Speech Detection
5 Conclusion
References
Automatic Signature-Based Writer Identification in Mixed-Script Scenarios*-6pt
1 Introduction
2 Proposed Method
2.1 Proposed Architecture
3 Experiments
3.1 Dataset
3.2 Experimental Framework and Results with Ablations Studies on Parameters
3.3 Comparative Study with State-of-the-Art and Analysis
3.4 Error Analysis
4 Conclusions and Future Directions
References
Indexing and Retrieval of Documents
Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting
1 Introduction
2 Related Work
2.1 Word Spotting
2.2 Ranking Metrics
3 Architecture
3.1 Problem Formulation
3.2 Encoder Networks
3.3 Learning Objectives
3.4 End-to-End Training
4 Experimental Evaluation
4.1 Implementation Details
4.2 Experimental Discussion
5 Conclusions
References
A-VLAD: An End-to-End Attention-Based Neural Network for Writer Identification in Historical Documents
1 Introduction
2 Related Work
2.1 Handcrafted Feature-Based Methods
2.2 Deep Neural Network-Based Methods
3 Proposed Method
3.1 CNN-Based Feature Extraction
3.2 Attention Classifier
3.3 A-VLAD Core Architecture
3.4 Online Triplet Training
4 Experiments
4.1 Datasets
4.2 Evaluation Methods
4.3 A-VLAD Configuration
5 Results and Discussion
5.1 Hyperparameter Finetuning
5.2 Performance on HisFragIR20 Dataset
6 Conclusion
References
Manga-MMTL: Multimodal Multitask Transfer Learning for Manga Character Analysis
1 Introduction
2 Related Work
3 Proposed Method
3.1 Overview
3.2 Learning Uni-modal Feature Extractor
3.3 Learning Multimodal Feature Extractor
3.4 Manga M-MTL Modal for Comic Character Analysis Tasks
3.5 Implementation Details
4 Results
4.1 Experiment Protocol
4.2 Multimodal vs. Uni-modal Models
4.3 Boosting with Simple Learnt Features Combination
5 Conclusion
References
Probabilistic Indexing and Search for Hyphenated Words
1 Introduction
2 Hyphenation in Historical Manuscripts
3 Probabilistic Indexing of Handwritten Text Images
4 Proposed Probabilistic Hyphenation Model
4.1 Simplifications
4.2 HwF Prediction: Optical and Language Modeling
4.3 Geometric Reasoning
4.4 Unite Prefix/Suffix HwF Pairs
5 Dataset, Assessment and Empirical Settings
5.1 Dataset
5.2 Evaluation Measures
5.3 Query Selection
5.4 Experimental Settings
6 Experiments and Results
6.1 Model Assessment Through HTR Performance
6.2 Hyphenated Word Probabilistic Indexing and Search Results
6.3 Live Demonstrator and Search Examples
7 Conclusion
References
Physical and Logical Layout Analysis
SandSlide: Automatic Slideshow Normalization
1 Introduction
2 Background
2.1 Slideshow Structure
2.2 PDF to PPT Converters
2.3 Automatic Document Annotation
3 Slideshow Normalisation
3.1 Problem Statement
3.2 Scope
4 SandSlide: Automatic Slideshow Normalization
4.1 Detecting and Labeling Objects in Slides
4.2 Qualitative Representation
4.3 Searching for Slide Layouts
5 Evaluation
5.1 Experimental Setup
5.2 Results
5.3 Rebuilding Slides
6 Conclusion and Future Work
References
Digital Editions as Distant Supervision for Layout Analysis of Printed Books
1 Introduction
2 Related Work
3 Analyzing Ground Truth Markup
4 Annotation by Forced Alignment
5 Experiments
5.1 Models
5.2 Pixel-Level Evaluations
5.3 Word-Level Evaluations
5.4 Region-Level Evaluations
5.5 Improving Accuracy with Self-training
5.6 Correlation of Word/Region-Level and Pixel-Level Metrics
6 Conclusions
References
Palmira: A Deep Deformable Network for Instance Segmentation of Dense and Uneven Layouts in Handwritten Manuscripts
1 Introduction
2 Related Work
3 Indiscapes2
4 Our Layout Parsing Network (Palmira)
4.1 Vanilla Mask-RCNN
4.2 Modification-1: Deformable Convolutions in Backbone
4.3 Modification-2: Deforming the Spatial Grid in Mask Head
4.4 Implementation Details
5 Experimental Setup
5.1 Baselines
5.2 Evaluation Setup
5.3 Evaluation Measures
6 Results
7 Conclusion
References
Page Layout Analysis System for Unconstrained Historic Documents
1 Introduction
2 Joint Text Line and Block Detection
2.1 ParseNet Architecture and Training
2.2 Text Baseline Detection
2.3 Text Line Polygon Estimation
2.4 Clustering Text Lines into Text Blocks
3 Multi-orientation Text Detection
4 Experiments
4.1 CBAD 2019 Text Baseline Detection
4.2 PERO Dataset Layout Analysis
4.3 Czech Newspaper OCR Evaluation
5 Discussion
6 Conclusion
References
Improved Graph Methods for Table Layout Understanding
1 Introduction
2 Background
3 Proposed Approach
3.1 Building the Initial Graph
3.2 Graph Neural Networks
3.3 Node and Edge Features
4 Experimental Settings and Datasets
4.1 Datasets
4.2 Improved Graphs Parameters
4.3 GNN Parameters
4.4 Assessment Measures
5 Results
5.1 Oracle Results
5.2 GNN Results
6 Discusion
7 Conclusions
References
Unsupervised Learning of Text Line Segmentation by Differentiating Coarse Patterns
1 Introduction
2 Related Work
3 Method
3.1 Deep Convolutional Network
3.2 Pseudo-RGB Image
3.3 Energy Minimization
4 Experiments
4.1 Data
4.2 Baseline Experiment
4.3 Effect of Patch Size (p)
4.4 Effect of Central Window Size (w)
4.5 Patch Saliency Visualization
4.6 Limitations
5 Results
5.1 Results on the VML-AHTE Dataset
5.2 Results on the ICDAR2017 Dataset
6 Conclusion
References
Recognition of Tables and Formulas
Rethinking Table Structure Recognition Using Sequence Labeling Methods
1 Introduction
2 Related Work
2.1 Table Structure Recognition
2.2 Sequence Labeling
3 Method
3.1 Encoder
3.2 Decoder
4 Data Preparation and Evaluation Metric
4.1 Data Preparation
4.2 Evaluation Metric
5 Experiments and Result
6 Conclusion
References
TabLeX: A Benchmark Dataset for Structure and Content Information Extraction from Scientific Tables
1 Introduction
2 The Current State of the Research
3 The TabLeX Dataset
3.1 Data Acquisition
3.2 Data Processing Pipeline
3.3 Dataset Statistics
4 Evaluation Metrics
5 Experiments
5.1 Implementation Details
5.2 Results
6 Conclusion
References
Handwritten Mathematical Expression Recognition with Bidirectionally Trained Transformer
1 Introduction
2 Related Work
2.1 HMER Methods
2.2 Transformer
2.3 Right-to-Left Language Modeling
3 Methodology
3.1 CNN Encoder
3.2 Positional Encoding
3.3 Transformer Decoder
3.4 Bidirectional Training Strategy
4 Implementation Details
4.1 Networks
4.2 Training
4.3 Inferencing
5 Experiments
5.1 Datasets
5.2 Compare with State-of-the-Art Results
5.3 Ablation Study
5.4 Case Study
6 Conclusion
References
TabAug: Data Driven Augmentation for Enhanced Table Structure Recognition
1 Introduction
2 Related Work
3 Methodology
3.1 Augmentation Operations
3.2 Augmentation Sub-operations
3.3 Augmentation Pipeline
4 Experiments and Results
4.1 Ground Truth
4.2 Performance Measures
4.3 Results and Analysis
5 Conclusion
References
An Encoder-Decoder Approach to Handwritten Mathematical Expression Recognition with Multi-head Attention and Stacked Decoder
1 Introduction
2 Our Approach
2.1 WAP-Based HMER Model
2.2 CNN-Transformer Based HMER Model
2.3 WAP-based HMER Model with MHA and Stacked Decoder
3 Experiments
3.1 Experimental Setup
3.2 Comparison of WAP and CNN-Transformer Baseline Models
3.3 Effect of MHA
3.4 Effect of Stacked Decoder
3.5 Comparison with Other HMER Systems and Error Analysis
4 Summary
References
Global Context for Improving Recognition of Online Handwritten Mathematical Expressions
1 Introduction
2 Related Works
2.1 Tree-Based BLSTM for HME Recognition
2.2 Spatial Relations Classification
2.3 Symbol Recognition with Bidirectional Context
3 Our Method
3.1 Overview of the Proposed Method
3.2 Sequential Model for Segmentation, Recognition, and Relation Classification
3.3 Deep BLSTM for Segmentation, Recognition, and Relation Classification
3.4 Constraint for Output at Precise Time Steps
3.5 Training Path Extractions
3.6 Symbol Level Parse Tree
4 Evaluation
4.1 Dataset
4.2 Setup for Experiments
4.3 Experiments and Results
5 Conclusion
References
Image-Based Relation Classification Approach for Table Structure Recognition
1 Introduction
2 Related Works
3 Problem Setting
4 Observations
5 Description of Our Approach
5.1 Preprocessor
5.2 Model
5.3 Data Augmentation
5.4 Scalability
6 Experiments
6.1 Dataset
6.2 Evaluation Metrics
6.3 Baselines
6.4 Experimental Details
6.5 Performances on the Full Data
6.6 Ablation Studies
6.7 Performances on the Small Data
7 Conclusion and Discussion
References
Image to LaTeX with Graph Neural Network for Mathematical Formula Recognition
1 Introduction
2 Related Work
2.1 Tree Based Methods
2.2 Grammar Based Methods
2.3 Learning Based Methods
3 Proposed Method
3.1 Problem Formulation
3.2 Graph Construction
3.3 Encoder
3.4 Decoder
4 Experiments
4.1 Datasets and Preprocessing
4.2 Implementation Details
4.3 Evaluation Metrics
5 Results
5.1 Performance Comparison
5.2 Robustness Analysis
5.3 Contribution Analysis
5.4 Case Study
6 Conclusion
References
NLP for Document Understanding
A Novel Method for Automated Suggestion of Similar Software Incidents Using 2-Stage Filtering: Findings on Primary Data
1 Introduction
2 Related Work
3 The Proposed Method
3.1 Class Labelling and Dataset Creation
3.2 Data Preprocessing
3.3 Text Vectorization
3.4 Removal of Imbalance
3.5 Classification Model
3.6 Classification Performance Analysis
3.7 Heuristic Definition
3.8 Calculating Thresholds
4 Experimental Setup and Evaluation
4.1 TTM Reduction
5 Discussions
6 Conclusion
References
Research on Pseudo-label Technology for Multi-label News Classification
1 Introduction
2 Related Work
3 Method
3.1 Text Representation
3.2 Text Label Matching
3.3 Pseudo-label Generation
3.4 Weighted Loss
4 Experiments
4.1 Datasets
4.2 Experimental Setting
5 Result and Analysis
5.1 Model Comparison
5.2 Pseudo-label Matching Threshold Screening
5.3 Pseudo-label Generation Strategy Verification
5.4 Pseudo-label Loss Weight Screening
6 Conclusion
References
Information Extraction from Invoices
1 Introduction
2 Related Work
3 Dataset
4 Methodology
4.1 Feature Extraction
4.2 Data Extraction Using the NER-Based Method
4.3 Data Extraction Using the Class-Based Method
5 Results
6 Conclusion
References
Are You Really Complaining? A Multi-task Framework for Complaint Identification, Emotion, and Sentiment Classification
1 Introduction
2 Related Work
3 Proposed Methodology
3.1 Problem Definition
3.2 BERT-Based Shared Private Multi-task Framework (BERT-SPMF)
4 Experiments, Results, and Analysis
4.1 Dataset
4.2 Deep Learning Baselines
4.3 Experimental Setup
4.4 Results and Discussion
4.5 Error Analysis
5 Conclusion and Future Work
References
Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformer
1 Introduction
2 Related Works
3 Model Architecture
4 Regularization Techniques
5 Experiments
5.1 Training Procedure
5.2 Results
6 Ablation Study
7 Summary
References
Data Centric Domain Adaptation for Historical Text with OCR Errors
1 Introduction
2 Methods
2.1 Architecture
2.2 Noise Methods
2.3 Embeddings
3 Experiments
3.1 Data
3.2 Baselines
4 Results and Discussion
4.1 In-domain Setup
4.2 Cross-Domain Setup
5 Related Work
6 Conclusion
A Appendix
References
Temporal Ordering of Events via Deep Neural Networks
1 Introduction
2 State of the Art
3 Proposed Model
3.1 Shortest Dependency Path Branch
3.2 Sentence Sequence Branch
3.3 Classification Module
3.4 Training Model
4 Experiments
4.1 Dataset
4.2 Model Implementation and Training
4.3 Results and Model Analysis
5 Conclusion and Future Work
References
Document Collection Visual Question Answering
1 Introduction
2 Related Work
2.1 Document Understanding
2.2 Document Retrieval
2.3 Visual Question Answering
3 DocCVQA Dataset
3.1 Data Collection
3.2 Statistics and Analysis
3.3 Evaluation Metrics
4 Baselines
4.1 Text Spotting + QA
4.2 Database Approach
5 Results
5.1 Evidences
5.2 Answers
6 Conclusions and Future Work
References
Dialogue Act Recognition Using Visual Information
1 Introduction
2 Related Work
3 Model Architectures
3.1 Visual Model
3.2 Text Model
3.3 Joint Model
4 Dataset
4.1 Image Dataset Acquisition
4.2 Utterance Segmentation
5 Experiments
5.1 Comparison with SoTA
5.2 OCR Experiment
5.3 Image Embedding Dimension
5.4 Visual Model Experiment
5.5 Text Model Experiment
5.6 Joint Model Experiment
6 Conclusions
References
Are End-to-End Systems Really Necessary for NER on Handwritten Document Images?
1 Introduction
2 Related Work
3 Datasets
3.1 Esposalles
3.2 Synthetic Groningen Meaning Bank
3.3 George Washington
3.4 IAM DB
4 Two-Staged Named Entity Recognition
5 Experiments
5.1 Evaluation Protocol
5.2 Results
5.3 Discussion
6 Conclusion
References
Training Bi-Encoders for Word Sense Disambiguation
1 Introduction
2 Related Work and Motivation
3 Datasets and Preprocessing
3.1 Source Datasets
3.2 Data Preprocessing
3.3 Oversampling
4 Model Training
4.1 Base Model
4.2 Context-Gloss Training
4.3 Context-Hypernym Gloss Training
4.4 Context-Gloss and Context-Hypernym Multi-Task Training
4.5 Triplet Training
5 Experiments
6 Performance on Unseen Synsets
7 Training Parameters
8 Conclusion and Future Work
References
DeepCPCFG: Deep Learning and Context Free Grammars for End-to-End Information Extraction
1 Introduction
2 Related Work
3 Problem Description
4 Approach
4.1 Parsing
4.2 Learning
4.3 Compatible Tree for Structured Prediction
5 Experiments
5.1 Results on CORD
5.2 Results on Invoices
6 Conclusion
References
Consideration of the Word's Neighborhood in GATs for Information Extraction in Semi-structured Documents
1 Introduction
2 Related Work
3 Proposed Method
3.1 SSD Representation
3.2 Graph Modeling
3.3 Word Modeling
3.4 Specific Graph: GAT
4 Experiments
4.1 Datasets
4.2 Implementation Details
4.3 Experimental Results
5 Conclusion
References
Author Index