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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 : Summit, SACH 2006, College Park, MD, USA, September 27-28, 2006, Selected Papers / / edited by David Doermann, Stefan Jaeger
| Arabic and Chinese Handwriting Recognition : Summit, SACH 2006, College Park, MD, USA, September 27-28, 2006, Selected Papers / / edited by David Doermann, Stefan Jaeger |
| Edizione | [1st ed. 2008.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2008 |
| Descrizione fisica | 1 online resource (VIII, 279 p.) |
| Disciplina | 006.424 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Image processing - Digital techniques
Computer vision Linguistics Pattern recognition systems Database management Information storage and retrieval systems Computer Imaging, Vision, Pattern Recognition and Graphics Computer Vision Automated Pattern Recognition Database Management Information Storage and Retrieval |
| 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, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : 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, Faisal Shafait
| Frontiers in Handwriting Recognition : 18th International Conference, ICFHR 2022, Hyderabad, India, December 4–7, 2022, Proceedings / / edited by Utkarsh Porwal, Alicia Fornés, Faisal Shafait |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (567 pages) |
| Disciplina |
006.424
006.425 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Pattern recognition systems
Database management Information storage and retrieval systems Machine learning Natural language processing (Computer science) Social sciences - Data processing Automated Pattern Recognition Database Management Information Storage and Retrieval Machine Learning Natural Language Processing (NLP) Computer Application in Social and Behavioral Sciences |
| ISBN |
9783031216480
3031216482 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Historical Document Processing -- A Few Shot Multi-Representation Approach for N-gram Spotting in Historical Manuscripts -- Text Edges Guided Network for Historical Document Super Resolution -- CurT: End-to-End Text Line Detection in Historical Documents with Transformers -- Date Recognition in Historical Parish Records -- Improving Isolated Glyph Classification Task for Palm leaf Manuscripts -- Signature Verification and Writer Identification -- Impact of Type of Convolution Operation on Performance of Convolutional Neural Networks for Online Signature Verification -- COMPOSV++: Light Weight Online Signature Verification Framework through Compound Feature Extraction and Few-shot Learning -- Finger-Touch Direction Feature Using a Frequency Distribution in the Writer Verification Base on Finger-Writing of a Simple Symbol -- Self-Supervised Vision Transformers with Data Augmentation Strategies using Morphological Operations for Writer Retrieval -- EAU-Net: A New Edge-Attention based U-Net for Nationality Identification -- Progressive Multitask Learning Network for Online Chinese Signature Segmentation and Recognition -- Symbol and Graphics Recognition -- Musigraph: Optical Music Recognition through Object Detection and Graph Neural Network -- Combining CNN and Transformer as Encoder to Improve End-to-end Handwritten Mathematical Expression Recognition Accuracy -- A Vision Transformer based Scene Text Recognizer with Multi-Grained Encoding and Decoding -- Spatial Attention and Syntax Rule Enhanced Tree Decoder for Offline Handwritten Mathematical Expression Recognition -- Handwriting Recognition and Understanding -- FPRNet: End-to-end Full-page Recognition Model for Handwritten Chinese Essay -- Active Transfer Learning for Handwriting Recognition -- Recognition-free Question Answering on Handwritten Document Collections -- Handwriting recognition and automatic scoring for descriptive answers in Japanese language tests -- A Weighted Combination of Semantic and Syntactic Word Image Representations -- Combining Self-Training and Minimal Annotations for Handwritten Word Recognition -- Script-Level Word Sample Augmentation for Few-shot Handwritten Text Recognition -- Towards understanding and improving handwriting with AI -- ChaCo: Character Contrastive Learning for Handwritten Text Recognition -- Enhancing Indic Handwritten Text Recognition using Global Semantic Information -- Yi Characters Online Handwriting Recognition Models Based on Recurrent Neural Network: RnnNet-Yi and ParallelRnnNet-Yi -- Self-Attention Networks for Non-Recurrent Handwritten Text Recognition -- An Efficient Prototype-based Model for Handwritten Text Recognition with Multi-Loss Fusion -- Handwriting Datasets and Synthetic Handwriting Generation -- Urdu Handwritten Ligature Generation using Generative Adversarial Networks (GANs) -- SCUT-CAB: A New Benchmark Dataset of Ancient Chinese Books with Complex Layouts for Document Layout Analysis -- A Benchmark Gurmukhi Handwritten Character Dataset: Acquisition, Compilation, and Recognition -- Synthetic Data Generation for Semantic Segmentation of Lecture Videos -- Generating synthetic styled Chu Nom characters -- UOHTD: Urdu Offline Handwritten Text Dataset -- Document Analysis and Processing -- DAZeTD: Deep Analysis of Zones in Torn Documents -- CNN-based Ruled Line Removal in Handwritten Documents -- Complex Table Structure Recognition in the Wild using Transformer and Identity Matrix-based Augmentation. |
| Record Nr. | UNINA-9910632468703321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
ICDAR 2005: Eighth International Conference on Document Analysis and Recognition (29 August - 01September 2005/Seoul, Korea)
| ICDAR 2005: Eighth International Conference on Document Analysis and Recognition (29 August - 01September 2005/Seoul, Korea) |
| Pubbl/distr/stampa | [Place of publication not identified], : IEEE Computer Society Press, 2005 |
| Descrizione fisica | 1 online resource (1292 pages) |
| Disciplina | 006.424 |
| Soggetto topico | Optical character recognition |
| ISBN |
9781538600764
1538600765 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910142345603321 |
| [Place of publication not identified], : IEEE Computer Society Press, 2005 | ||
| 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 | ||
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