11108nam 2200517 450 99650006190331620231110220126.03-031-21648-2(MiAaPQ)EBC7146398(Au-PeEL)EBL7146398(CKB)25461606000041(PPN)266348777(EXLCZ)992546160600004120230408d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierFrontiers in handwriting recognition 18th international conference, ICFHR 2022, Hyderabad, India, December 4-7, 2022, proceedings /edited by Utkarsh Porwal, Alicia Fornés, and Faisal ShafaitCham, Switzerland :Springer,[2022]©20221 online resource (567 pages)Lecture Notes in Computer Science ;v.13639Print version: Porwal, Utkarsh Frontiers in Handwriting Recognition Cham : Springer International Publishing AG,c2022 9783031216473 Includes bibliographical references and index.Intro -- Preface -- Organization -- Contents -- Historical Document Processing -- A Few Shot Multi-representation Approach for N-Gram Spotting in Historical Manuscripts -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 The Base Architecture -- 3.2 The Multi-modal Architecture -- 3.3 Multi-modal Architecture with Early Fusion -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Evaluation Metrics -- 4.3 Results and Discussion -- 5 Conclusion -- References -- Text Edges Guided Network for Historical Document Super Resolution -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 4 Method -- 4.1 Model Framework -- 4.2 Objective Function -- 5 Experiment -- 5.1 Data Preparation -- 5.2 Hyperparameters Tuning Using Grid Search -- 5.3 Super-Resolution Evaluation -- 6 Conclusion -- References -- CurT: End-to-End Text Line Detection in Historical Documents with Transformers -- 1 Introduction -- 2 Related Work -- 2.1 Transformers for Computer Vision -- 2.2 DETR and Variants -- 2.3 Text Baseline Detection -- 3 Contribution -- 4 The CurT Model -- 4.1 Text Line Data Model -- 4.2 Curve Detection Set Prediction Loss -- 4.3 CurT Architecture -- 5 Experiments -- 5.1 Dataset and Evaluation Protocol -- 5.2 Implementation Details -- 5.3 Overall Performance -- 5.4 Ordered Prediction -- 5.5 Further Extensions -- 6 Conclusion -- References -- Date Recognition in Historical Parish Records -- 1 Introduction -- 2 Data -- 3 Date Recognition -- 4 Experiments -- 4.1 Data Splits -- 4.2 Segmentation -- 4.3 Models -- 4.4 Evaluation Metrics -- 5 Results and Analysis -- 6 Related Work -- 7 Future Work -- 8 Conclusion -- References -- Improving Isolated Glyph Classification Task for Palm Leaf Manuscripts -- 1 Introduction -- 2 Palm Leaf Manuscripts from Southeast Asia -- 2.1 Corpus and Languages -- 2.2 Challenges of Isolated Glyph Datasets -- 3 Overall Frameworks.3.1 Data Pattern Generations -- 3.2 Image Enhancement for Palm Leaf Manuscripts (IEPalm) -- 3.3 Training CNNs and ViTs -- 4 Experimental Setups and Results -- 4.1 Implementation Settings -- 4.2 Results -- 5 Conclusion -- References -- Signature Verification and Writer Identification -- Impact of Type of Convolution Operation on Performance of Convolutional Neural Networks for Online Signature Verification -- 1 Introduction -- 2 Related Work -- 3 Proposed OSV Framework -- 3.1 Input Representation, Type of Convolution and Order of Convolution -- 3.2 Analyzing the Impact of Signature Length -- 3.3 Further Improvement of Input Representation -- 4 Comparison with SOTA Methods -- 5 Conclusion and Future Work -- References -- COMPOSV++: Light Weight Online Signature Verification Framework Through Compound Feature Extraction and Few-Shot Learning -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Online Signature Verification Framework -- 3.1 Proposed Novel Dimensionality Reduction Algorithm -- 3.2 Proposed Separable Convolution Operation Based OSV Framework: -- 4 Experimentation Analysis and Results -- 5 Conclusion and Future Work -- References -- Finger-Touch Direction Feature Using a Frequency Distribution in the Writer Verification Base on Finger-Writing of a Simple Symbol -- 1 Introduction -- 2 Writer Verification Based on Finger-Writing of a Simple Symbol -- 3 Introduction of Finger-Touching Direction Feature -- 3.1 Finger-Touching Direction -- 3.2 Evaluation of Verification Performance -- 3.3 Considerations -- 4 Introduction of Preprocessing -- 5 Frequency Distribution as a New Feature -- 6 Conclusions -- References -- Self-supervised Vision Transformers with Data Augmentation Strategies Using Morphological Operations for Writer Retrieval -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Preprocessing -- 3.2 Vision Transformer.3.3 Morphological Operations -- 3.4 Self-supervised Training -- 3.5 Page Descriptor and Retrieval -- 4 Experiments -- 4.1 Historical-WI Dataset -- 4.2 Evaluation -- 4.3 Results -- 5 Conclusion -- References -- EAU-Net: A New Edge-Attention Based U-Net for Nationality Identification -- 1 Introduction -- 2 Related Work -- 3 Proposed Model -- 3.1 Edge-Attention Based U-Net for Edge Detection -- 3.2 Nationality/Ethnicity Identification -- 4 Experimental Results -- 4.1 Ablation Study -- 4.2 Experiments on Edge Detection -- 4.3 Experiments on Classification of Nationality -- 4.4 Gender Classification -- 4.5 Error Analysis -- 5 Conclusion and Future Work -- References -- Progressive Multitask Learning Network for Online Chinese Signature Segmentation and Recognition -- 1 Introduction -- 2 Methodology -- 2.1 Overview -- 2.2 Dual Channel Stroke Feature Extraction Block (DSF-Block) -- 2.3 Stacked Transformer Encoder Block (STE-Block) -- 2.4 Progressive Multitask Interaction Block (PMI-Block) -- 2.5 Training Objective -- 3 Experiments -- 3.1 Database -- 3.2 Evaluation Metrics -- 3.3 Implementation Details -- 3.4 Qualitative Results -- 3.5 Quantitative Results -- 3.6 Ablation Studies -- 4 Conclusion -- References -- Symbol and Graphics Recognition -- Musigraph: Optical Music Recognition Through Object Detection and Graph Neural Network -- 1 Introduction -- 2 Related Work -- 2.1 Optical Music Recognition (OMR) -- 2.2 Graph Neural Network (GNN) -- 3 The Musigraph Model -- 3.1 Object Detector -- 3.2 Graph Neural Network -- 4 Dataset -- 5 Experimental Validation -- 5.1 Object Detection Results -- 5.2 Graph Neural Network Results -- 6 Conclusions and Future Work -- References -- Combining CNN and Transformer as Encoder to Improve End-to-End Handwritten Mathematical Expression Recognition Accuracy -- 1 Introduction -- 2 Methodology -- 2.1 Baseline System.2.2 Tandem Approach -- 2.3 Parallel Approach -- 2.4 Mixing Approach -- 3 Experimental Result -- 3.1 Experimental Setup -- 3.2 Overall Results -- 3.3 Effects of Number of Transformer Encoder Layers to Tandem Approach -- 3.4 Effects of Number of Transformer Encoder Layers to Parallel Approach -- 3.5 Effects of Number of Attention Heads to Mixing Approach -- 4 Conclusion -- References -- A Vision Transformer Based Scene Text Recognizer with Multi-grained Encoding and Decoding -- 1 Introduction -- 2 Related Works -- 2.1 Scene Text Recognition -- 2.2 Vision Transformer -- 2.3 Self-supervised Learning -- 3 Method -- 3.1 Pipeline -- 3.2 Two-Stage Encoder -- 3.3 Joint Decoder -- 3.4 MAE with Focusing Mechanism -- 3.5 Objective Functions and Training Strategies -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Comparisons with State-of-the-Arts -- 4.4 Ablation Studies -- 4.5 Experiments on Occlusion Scene Text -- 5 Conclusions -- References -- Spatial Attention and Syntax Rule Enhanced Tree Decoder for Offline Handwritten Mathematical Expression Recognition -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 Child Node Prediction Module -- 3.2 Spatial Attention-Based Parent Node Prediction Module -- 3.3 Syntax Rule-Based Relation Prediction Module -- 3.4 Total Loss -- 4 Experiments -- 4.1 Dataset -- 4.2 Implementation Details -- 4.3 Ablation Experiment -- 4.4 Performance Comparison -- 5 Conclusion -- References -- Handwriting Recognition and Understanding -- FPRNet: End-to-End Full-Page Recognition Model for Handwritten Chinese Essay -- 1 Introduction -- 2 Related Works -- 2.1 Segmentation-Based Approaches -- 2.2 Segmentation-Free Approaches -- 3 Architecture -- 3.1 Encoder -- 3.2 Decoder -- 3.3 Order-Align Strategy -- 4 Experiments and Results -- 4.1 Dataset -- 4.2 Experimental Setup -- 4.3 Experimental Results.5 Conclusion -- References -- Active Transfer Learning for Handwriting Recognition -- 1 Introduction -- 2 Related Work -- 2.1 Transfer Learning -- 2.2 Active Learning -- 2.3 Active Transfer Learning -- 3 Methodology -- 3.1 Model Weights Initialization -- 3.2 Active Learning Sample Selection -- 3.3 Supervised Training -- 3.4 Model Evaluation -- 4 Results -- 4.1 Methods Comparison -- 4.2 Incremental Iterative Training -- 4.3 Selection of Pre-trained Model Weights -- 5 Conclusion -- References -- Recognition-Free Question Answering on Handwritten Document Collections -- 1 Introduction -- 2 Related Work -- 2.1 Document Retrieval -- 2.2 Question Answering -- 3 Method -- 3.1 Query and Document Representation -- 3.2 Retrieval -- 3.3 Question Answering -- 4 Experiments -- 4.1 Dataset -- 4.2 Implementation Details -- 4.3 Results -- 5 Conclusions -- References -- Handwriting Recognition and Automatic Scoring for Descriptive Answers in Japanese Language Tests -- 1 Introduction -- 2 Related Works -- 3 Handwritten Japanese Answer Dataset -- 3.1 Handwritten Text-Line Segmentation -- 3.2 Splitting and Labeling Samples -- 3.3 Statistics -- 4 Handwritten Answer Recognition and Automatic Scoring -- 4.1 Handwritten Answer Recognition -- 4.2 Automatic Scoring -- 5 Experiment Results -- 5.1 Performance of Recognition Model -- 5.2 Performance of Automatic Scoring Model -- 6 Conclusions -- References -- A Weighted Combination of Semantic and Syntactic Word Image Representations -- 1 Introduction -- 2 Related Work -- 2.1 Traditional Word Spotting -- 2.2 Semantic Word Spotting -- 2.3 Word Embeddings -- 3 Method -- 3.1 Word Image Representation -- 3.2 Weighted Combination Approaches -- 3.3 Normalization -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Evaluation Protocol -- 4.4 Normalization -- 4.5 Results -- 5 Conclusions -- References.Combining Self-training and Minimal Annotations for Handwritten Word Recognition.Lecture Notes in Computer Science Optical character recognitionOptical character recognition.006.424Porwal UtkarshFornés AliciaShafait FaisalMiAaPQMiAaPQMiAaPQBOOK996500061903316Frontiers in handwriting recognition3084153UNISA02402nam 2200577 a 450 991078898670332120230721033629.01-282-41338-497866124133841-4438-0735-4(CKB)3390000000008839(EBL)1114518(OCoLC)827209029(SSID)ssj0000443950(PQKBManifestationID)12122946(PQKBTitleCode)TC0000443950(PQKBWorkID)10462375(PQKB)10308578(MiAaPQ)EBC1114518(Au-PeEL)EBL1114518(CaPaEBR)ebr10655344(CaONFJC)MIL241338(OCoLC)515542592(FINmELB)ELB137053(EXLCZ)99339000000000883920091019d2009 uy 0engur|n|---|||||txtccrVia media philosophy[electronic resource] holiness unto truth : intersections between Wesleyan and Roman Catholic voices /edited by L. Bryan WilliamsNewcastle upon Tyne Cambridge Scholars2009Newcastle upon Tyne :Cambridge Scholars,2009.1 online resource (242 p.)Description based upon print version of record.1-4438-0506-8 Includes bibliographical references and index.pt. 1. Via media philosophy -- pt. 2. Contrasting voices -- pt. 3. Social issues -- pt. 4. Contemporary perspectives.This book, via media philosophy: Holiness unto Truth. Conversations between Wesleyan and Roman Catholic Voices, records the first formal philosophical conversations between Wesleyan and Roman Catholic philosophers and theologians. Although the Methodist community has developed numerous points of intersection with Roman Catholic counterparts, authors from smaller Wesleyan/Holiness groups along with Roman Catholic writers now offer new philosophical conversations. This book begins that convers...TruthReligious aspectsChristianityTruthReligious aspectsChristianity.230/.7Williams L. Bryan1541229MiAaPQMiAaPQMiAaPQBOOK9910788986703321Via media philosophy3793266UNINA