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2018 IEEE 2nd International Workshop on Arabic and Derived Script Analysis and Recognition : 12-14 March 2018, London, UK / / Institute of Electrical and Electronics Engineers
| 2018 IEEE 2nd International Workshop on Arabic and Derived Script Analysis and Recognition : 12-14 March 2018, London, UK / / Institute of Electrical and Electronics Engineers |
| Pubbl/distr/stampa | Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018 |
| Descrizione fisica | 1 online resource (107 pages) |
| Disciplina | 006.424 |
| Soggetto topico |
Optical character recognition
Arabic language - Data processing Image processing - Digital techniques |
| ISBN | 1-5386-1459-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996280091703316 |
| Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
2018 IEEE 2nd International Workshop on Arabic and Derived Script Analysis and Recognition : 12-14 March 2018, London, UK / / Institute of Electrical and Electronics Engineers
| 2018 IEEE 2nd International Workshop on Arabic and Derived Script Analysis and Recognition : 12-14 March 2018, London, UK / / Institute of Electrical and Electronics Engineers |
| Pubbl/distr/stampa | Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018 |
| Descrizione fisica | 1 online resource (107 pages) |
| Disciplina | 006.424 |
| Soggetto topico |
Optical character recognition
Arabic language - Data processing Image processing - Digital techniques |
| ISBN | 1-5386-1459-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910287957803321 |
| Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Arabic and Chinese handwriting recognition : SACH 2006 summit, College Park, MD, USA, September 27-28, 2006 : selected papers / / David Doermann, Stefan Jaeger (eds.)
| Arabic and Chinese handwriting recognition : SACH 2006 summit, College Park, MD, USA, September 27-28, 2006 : selected papers / / David Doermann, Stefan Jaeger (eds.) |
| Edizione | [1st ed. 2008.] |
| Pubbl/distr/stampa | Berlin, Germany ; ; New York, New York : , : Springer, , [2008] |
| Descrizione fisica | 1 online resource (VIII, 279 p.) |
| Disciplina | 006.424 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical character recognition devices
Chinese language - Writing - Data processing Writing, Arabic - Data processing |
| ISBN | 3-540-78199-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Visual Recognition of Arabic Handwriting: Challenges and New Directions -- A Review on Persian Script and Recognition Techniques -- Human Reading Based Strategies for Off-Line Arabic Word Recognition -- Versatile Search of Scanned Arabic Handwriting -- A Two-Tier Arabic Offline Handwriting Recognition Based on Conditional Joining Rules -- Databases and Competitions: Strategies to Improve Arabic Recognition Systems -- Handwritten Chinese Character Recognition: Effects of Shape Normalization and Feature Extraction -- How to Deal with Uncertainty and Variability: Experience and Solutions -- An Efficient Candidate Set Size Reduction Method for Coarse-Classification in Chinese Handwriting Recognition -- Techniques for Solving the Large-Scale Classification Problem in Chinese Handwriting Recognition -- Recent Results of Online Japanese Handwriting Recognition and Its Applications -- Segmentation-Driven Offline Handwritten Chinese and Arabic Script Recognition -- Multi-character Field Recognition for Arabic and Chinese Handwriting -- Multi-lingual Offline Handwriting Recognition Using Hidden Markov Models: A Script-Independent Approach -- Handwritten Character Recognition of Popular South Indian Scripts -- Ensemble Methods to Improve the Performance of an English Handwritten Text Line Recognizer. |
| Record Nr. | UNISA-996466074503316 |
| Berlin, Germany ; ; New York, New York : , : Springer, , [2008] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Arabic and Chinese handwriting recognition : SACH 2006 summit, College Park, MD, USA, September 27-28, 2006 : selected papers / / David Doermann, Stefan Jaeger (eds.)
| Arabic and Chinese handwriting recognition : SACH 2006 summit, College Park, MD, USA, September 27-28, 2006 : selected papers / / David Doermann, Stefan Jaeger (eds.) |
| Edizione | [1st ed. 2008.] |
| Pubbl/distr/stampa | Berlin, Germany ; ; New York, New York : , : Springer, , [2008] |
| Descrizione fisica | 1 online resource (VIII, 279 p.) |
| Disciplina | 006.424 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical character recognition devices
Chinese language - Writing - Data processing Writing, Arabic - Data processing |
| ISBN | 3-540-78199-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Visual Recognition of Arabic Handwriting: Challenges and New Directions -- A Review on Persian Script and Recognition Techniques -- Human Reading Based Strategies for Off-Line Arabic Word Recognition -- Versatile Search of Scanned Arabic Handwriting -- A Two-Tier Arabic Offline Handwriting Recognition Based on Conditional Joining Rules -- Databases and Competitions: Strategies to Improve Arabic Recognition Systems -- Handwritten Chinese Character Recognition: Effects of Shape Normalization and Feature Extraction -- How to Deal with Uncertainty and Variability: Experience and Solutions -- An Efficient Candidate Set Size Reduction Method for Coarse-Classification in Chinese Handwriting Recognition -- Techniques for Solving the Large-Scale Classification Problem in Chinese Handwriting Recognition -- Recent Results of Online Japanese Handwriting Recognition and Its Applications -- Segmentation-Driven Offline Handwritten Chinese and Arabic Script Recognition -- Multi-character Field Recognition for Arabic and Chinese Handwriting -- Multi-lingual Offline Handwriting Recognition Using Hidden Markov Models: A Script-Independent Approach -- Handwritten Character Recognition of Popular South Indian Scripts -- Ensemble Methods to Improve the Performance of an English Handwritten Text Line Recognizer. |
| Record Nr. | UNINA-9910483791603321 |
| Berlin, Germany ; ; New York, New York : , : Springer, , [2008] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Character recognition systems [[electronic resource] ] : a guide for students and practioners / / Mohamed Cheriet ... [et al.]
| Character recognition systems [[electronic resource] ] : a guide for students and practioners / / Mohamed Cheriet ... [et al.] |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley-Interscience, c2007 |
| Descrizione fisica | 1 online resource (360 p.) |
| Disciplina |
006.4/24
006.424 |
| Altri autori (Persone) | CherietM (Mohamed) |
| Soggetto topico | Optical character recognition devices |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-281-13472-4
9786611134723 0-470-17653-9 0-470-17652-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
CHARACTER RECOGNITION SYSTEMS; CONTENTS; Preface; Acknowledgments; List of Figures; List of Tables; Acronyms; 1 Introduction: Character Recognition, Evolution, and Development; 1.1 Generation and Recognition of Characters; 1.2 History of OCR; 1.3 Development of New Techniques; 1.4 Recent Trends and Movements; 1.5 Organization of the Remaining Chapters; References; 2 Tools for Image Preprocessing; 2.1 Generic Form-Processing System; 2.2 A Stroke Model for Complex Background Elimination; 2.2.1 Global Gray Level Thresholding; 2.2.2 Local Gray Level Thresholding
2.2.3 Local Feature Thresholding-Stroke-Based Model2.2.4 Choosing the Most Efficient Character Extraction Method; 2.2.5 Cleaning Up Form Items Using Stroke-Based Model; 2.3 A Scale-Space Approach for Visual Data Extraction; 2.3.1 Image Regularization; 2.3.2 Data Extraction; 2.3.3 Concluding Remarks; 2.4 Data Preprocessing; 2.4.1 Smoothing and Noise Removal; 2.4.2 Skew Detection and Correction; 2.4.3 Slant Correction; 2.4.4 Character Normalization; 2.4.5 Contour Tracing/Analysis; 2.4.6 Thinning; 2.5 Chapter Summary; References; 3 Feature Extraction, Selection, and Creation 3.1 Feature Extraction3.1.1 Moments; 3.1.2 Histogram; 3.1.3 Direction Features; 3.1.4 Image Registration; 3.1.5 Hough Transform; 3.1.6 Line-Based Representation; 3.1.7 Fourier Descriptors; 3.1.8 Shape Approximation; 3.1.9 Topological Features; 3.1.10 Linear Transforms; 3.1.11 Kernels; 3.2 Feature Selection for Pattern Classification; 3.2.1 Review of Feature Selection Methods; 3.3 Feature Creation for Pattern Classification; 3.3.1 Categories of Feature Creation; 3.3.2 Review of Feature Creation Methods; 3.3.3 Future Trends; 3.4 Chapter Summary; References; 4 Pattern Classification Methods 4.1 Overview of Classification Methods4.2 Statistical Methods; 4.2.1 Bayes Decision Theory; 4.2.2 Parametric Methods; 4.2.3 Nonparametric Methods; 4.3 Artificial Neural Networks; 4.3.1 Single-Layer Neural Network; 4.3.2 Multilayer Perceptron; 4.3.3 Radial Basis Function Network; 4.3.4 Polynomial Network; 4.3.5 Unsupervised Learning; 4.3.6 Learning Vector Quantization; 4.4 Support Vector Machines; 4.4.1 Maximal Margin Classifier; 4.4.2 Soft Margin and Kernels; 4.4.3 Implementation Issues; 4.5 Structural Pattern Recognition; 4.5.1 Attributed String Matching; 4.5.2 Attributed Graph Matching 4.6 Combining Multiple Classifiers4.6.1 Problem Formulation; 4.6.2 Combining Discrete Outputs; 4.6.3 Combining Continuous Outputs; 4.6.4 Dynamic Classifier Selection; 4.6.5 Ensemble Generation; 4.7 A Concrete Example; 4.8 Chapter Summary; References; 5 Word and String Recognition; 5.1 Introduction; 5.2 Character Segmentation; 5.2.1 Overview of Dissection Techniques; 5.2.2 Segmentation of Handwritten Digits; 5.3 Classification-Based String Recognition; 5.3.1 String Classification Model; 5.3.2 Classifier Design for String Recognition; 5.3.3 Search Strategies 5.3.4 Strategies for Large Vocabulary |
| Record Nr. | UNINA-9910145585503321 |
| Hoboken, N.J., : Wiley-Interscience, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Character recognition systems [[electronic resource] ] : a guide for students and practioners / / Mohamed Cheriet ... [et al.]
| Character recognition systems [[electronic resource] ] : a guide for students and practioners / / Mohamed Cheriet ... [et al.] |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley-Interscience, c2007 |
| Descrizione fisica | 1 online resource (360 p.) |
| Disciplina |
006.4/24
006.424 |
| Altri autori (Persone) | CherietM (Mohamed) |
| Soggetto topico | Optical character recognition devices |
| ISBN |
1-281-13472-4
9786611134723 0-470-17653-9 0-470-17652-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
CHARACTER RECOGNITION SYSTEMS; CONTENTS; Preface; Acknowledgments; List of Figures; List of Tables; Acronyms; 1 Introduction: Character Recognition, Evolution, and Development; 1.1 Generation and Recognition of Characters; 1.2 History of OCR; 1.3 Development of New Techniques; 1.4 Recent Trends and Movements; 1.5 Organization of the Remaining Chapters; References; 2 Tools for Image Preprocessing; 2.1 Generic Form-Processing System; 2.2 A Stroke Model for Complex Background Elimination; 2.2.1 Global Gray Level Thresholding; 2.2.2 Local Gray Level Thresholding
2.2.3 Local Feature Thresholding-Stroke-Based Model2.2.4 Choosing the Most Efficient Character Extraction Method; 2.2.5 Cleaning Up Form Items Using Stroke-Based Model; 2.3 A Scale-Space Approach for Visual Data Extraction; 2.3.1 Image Regularization; 2.3.2 Data Extraction; 2.3.3 Concluding Remarks; 2.4 Data Preprocessing; 2.4.1 Smoothing and Noise Removal; 2.4.2 Skew Detection and Correction; 2.4.3 Slant Correction; 2.4.4 Character Normalization; 2.4.5 Contour Tracing/Analysis; 2.4.6 Thinning; 2.5 Chapter Summary; References; 3 Feature Extraction, Selection, and Creation 3.1 Feature Extraction3.1.1 Moments; 3.1.2 Histogram; 3.1.3 Direction Features; 3.1.4 Image Registration; 3.1.5 Hough Transform; 3.1.6 Line-Based Representation; 3.1.7 Fourier Descriptors; 3.1.8 Shape Approximation; 3.1.9 Topological Features; 3.1.10 Linear Transforms; 3.1.11 Kernels; 3.2 Feature Selection for Pattern Classification; 3.2.1 Review of Feature Selection Methods; 3.3 Feature Creation for Pattern Classification; 3.3.1 Categories of Feature Creation; 3.3.2 Review of Feature Creation Methods; 3.3.3 Future Trends; 3.4 Chapter Summary; References; 4 Pattern Classification Methods 4.1 Overview of Classification Methods4.2 Statistical Methods; 4.2.1 Bayes Decision Theory; 4.2.2 Parametric Methods; 4.2.3 Nonparametric Methods; 4.3 Artificial Neural Networks; 4.3.1 Single-Layer Neural Network; 4.3.2 Multilayer Perceptron; 4.3.3 Radial Basis Function Network; 4.3.4 Polynomial Network; 4.3.5 Unsupervised Learning; 4.3.6 Learning Vector Quantization; 4.4 Support Vector Machines; 4.4.1 Maximal Margin Classifier; 4.4.2 Soft Margin and Kernels; 4.4.3 Implementation Issues; 4.5 Structural Pattern Recognition; 4.5.1 Attributed String Matching; 4.5.2 Attributed Graph Matching 4.6 Combining Multiple Classifiers4.6.1 Problem Formulation; 4.6.2 Combining Discrete Outputs; 4.6.3 Combining Continuous Outputs; 4.6.4 Dynamic Classifier Selection; 4.6.5 Ensemble Generation; 4.7 A Concrete Example; 4.8 Chapter Summary; References; 5 Word and String Recognition; 5.1 Introduction; 5.2 Character Segmentation; 5.2.1 Overview of Dissection Techniques; 5.2.2 Segmentation of Handwritten Digits; 5.3 Classification-Based String Recognition; 5.3.1 String Classification Model; 5.3.2 Classifier Design for String Recognition; 5.3.3 Search Strategies 5.3.4 Strategies for Large Vocabulary |
| Record Nr. | UNINA-9910830367403321 |
| Hoboken, N.J., : Wiley-Interscience, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Frontiers in handwriting recognition : 18th international conference, ICFHR 2022, Hyderabad, India, December 4-7, 2022, proceedings / / edited by Utkarsh Porwal, Alicia Fornés, and Faisal Shafait
| Frontiers in handwriting recognition : 18th international conference, ICFHR 2022, Hyderabad, India, December 4-7, 2022, proceedings / / edited by Utkarsh Porwal, Alicia Fornés, and Faisal Shafait |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (567 pages) |
| Disciplina | 006.424 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico | Optical character recognition |
| ISBN | 3-031-21648-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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. |
| Record Nr. | UNISA-996500061903316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Frontiers in handwriting recognition : 18th international conference, ICFHR 2022, Hyderabad, India, December 4-7, 2022, proceedings / / edited by Utkarsh Porwal, Alicia Fornés, and Faisal Shafait
| Frontiers in handwriting recognition : 18th international conference, ICFHR 2022, Hyderabad, India, December 4-7, 2022, proceedings / / edited by Utkarsh Porwal, Alicia Fornés, and Faisal Shafait |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (567 pages) |
| Disciplina | 006.424 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico | Optical character recognition |
| ISBN | 3-031-21648-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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. |
| Record Nr. | UNINA-9910632468703321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Multimedia Content Representation, Classification and Security : International workshops MRCS 2006 : Istanbul, Turkey, September 11-13, 2006 : proceedings / Bilge Gunsel...[et al.](eds.)
| Multimedia Content Representation, Classification and Security : International workshops MRCS 2006 : Istanbul, Turkey, September 11-13, 2006 : proceedings / Bilge Gunsel...[et al.](eds.) |
| Pubbl/distr/stampa | Berlin : Springer, copyr. 2006 |
| Descrizione fisica | XIX, 804 p. : ill. ; 20 cm |
| Disciplina | 006.424 |
| Collana | Lecture notes in computer science |
| Soggetto topico | Multimedia - Sicurezza - Congressi - Istanbul - 2006 |
| ISBN | 3-540-39392-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-990002972380203316 |
| Berlin : Springer, copyr. 2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Operations and Management in IP-Based Networks : 5th IEEE International Workshop on IP Operations and Management, IPOM 2005 : Barcelona, Spain, October 26-28, 2005 : proceedings / Thomas Magedanz, Edmundo R.M. Maderira, Petre Dini(eds.)
| Operations and Management in IP-Based Networks : 5th IEEE International Workshop on IP Operations and Management, IPOM 2005 : Barcelona, Spain, October 26-28, 2005 : proceedings / Thomas Magedanz, Edmundo R.M. Maderira, Petre Dini(eds.) |
| Pubbl/distr/stampa | Berlin : Springer, copyr. 2006 |
| Descrizione fisica | X, 211 p. : ill. ; 20 cm |
| Disciplina | 006.424 |
| Collana | Lecture notes in computer science |
| Soggetto topico | Elaboratori Elettronici - Programmazione - Congressi - Barcellona - 2005 |
| ISBN | 3-540-29356-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-990002975630203316 |
| Berlin : Springer, copyr. 2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
