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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.) |
| Autore | SACH 2006 Summit : <2006 |
| Pubbl/distr/stampa | Berlin [etc.] : Springer, copyr. 2008 |
| Descrizione fisica | VIII, 277 p. : ill. ; 24 cm |
| Disciplina | 006.425 |
| Collana | Lecture notes in computer science |
| Soggetto topico |
Riconoscimento delle forme - Congressi - College Park - 2006
Elaborazione dei dati - Congressi - College Park - 2006 |
| ISBN | 978-3-540-78198-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-990003106880203316 |
SACH 2006 Summit : <2006
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| Berlin [etc.] : Springer, copyr. 2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Chinese handwriting recognition : an algorithmic perspective / / Tonghua Su
| Chinese handwriting recognition : an algorithmic perspective / / Tonghua Su |
| Autore | Su Tonghua |
| Edizione | [1st ed. 2013.] |
| Pubbl/distr/stampa | Berlin ; ; Heidelberg, : Springer-Verlag, 2013 |
| Descrizione fisica | 1 online resource (xi, 124 pages) : illustrations (some color) |
| Disciplina | 006.425 |
| Collana | SpringerBriefs in electrical and computer engineering |
| Soggetto topico |
Computer vision
Optical pattern recognition |
| ISBN |
1-299-19758-2
3-642-31812-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- HIT-MW Database -- Integrated Segmentation-Recognition Strategy -- Segmentation-free Strategy: Basic Algorithms -- Segmentation-free Strategy: Advanced Algorithms. |
| Record Nr. | UNINA-9910437772103321 |
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Su Tonghua
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| Berlin ; ; Heidelberg, : Springer-Verlag, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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 | ||
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Multimodal Interactive handwritten text transcription [[electronic resource] /] / Verónica Romero, Alejandra Héctor Toselli, Enrique Vidal
| Multimodal Interactive handwritten text transcription [[electronic resource] /] / Verónica Romero, Alejandra Héctor Toselli, Enrique Vidal |
| Autore | Romer Verónica |
| Pubbl/distr/stampa | Singapore ; ; Hackensack, NJ, : World Scientific Pub, c2012 |
| Descrizione fisica | 1 online resource (180 p.) |
| Disciplina | 006.425 |
| Altri autori (Persone) |
ToselliAlejandro Héctor
VidalEnrique |
| Collana | Series in machine perception and artificial intelligence |
| Soggetto topico |
Writing - Data processing
Multimodal user interfaces (Computer systems) Human-computer interaction |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-283-59373-4
9786613906182 981-4390-34-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents; Preface; 1. Preliminaries; 1.1 Introduction; 1.2 State of the Art; 1.2.1 Optical Character Recognition; 1.2.2 Handwritten Text Recognition; 1.3 Formal Background; 1.3.1 Hidden Markov Models; Continuous HMM; Basic algorithms for HMMs; The Decoding Problem and the Viterbi Algorithm; The Learning Problem and the Baum-Welch Algorithm; 1.3.2 Language models: N-grams; n-grams modelled by a stochastic finite state automaton; 1.3.3 Interactive Pattern Recognition; 1.3.4 Word-graphs; 1.4 Assessing Computer Assisted Transcription of Handwritten Text Images; 2. Corpora; 2.1 Introduction
2.2 CS2.3 ODEC; 2.4 IAMDB; 2.5 UNIPEN; 3. Handwritten Text Recognition; 3.1 Introduction; 3.2 Off-line Handwritten Text Recognition; 3.2.1 Preprocessing; 3.2.2 Feature Extraction; 3.2.3 Recognition; 3.2.4 Experimental Framework; 3.2.5 Meta-parameter Adjustment Experiments; 3.2.6 Discussion of Results; 3.3 On-line Handwritten Text Recognition; 3.3.1 Preprocessing; 3.3.2 Feature Extraction; 3.3.3 Recognition; 3.3.4 Experimental Framework; 3.3.5 Results; 3.4 Summary and Conclusions; 4. Computer Assisted Transcription of Handwritten Text Images; 4.1 Introduction; 4.2 Formal Framework 4.3 Adapting the Language Model4.4 Searching; 4.4.1 Direct Viterbi-based Approach; 4.4.2 Word-graph based Approach; 4.4.2.1 Error-correction parsing; 4.5 Increasing Interaction Ergonomy; 4.5.1 Language Modelling and Search; 4.6 Interacting at the Character Level; 4.6.1 Language Modelling and Search; 4.7 Experimental Framework; 4.7.1 Assessment Measures; 4.7.2 Parameters and Meta-Parameters; 4.8 Results; Direct Viterbi-based approach; Word-graph based approach; Using Pointer Actions in the CATTI interaction process (PA- CATTI); CATTI at the character level; 4.9 Conclusions and Future Work 5. Multimodal Computer Assisted Transcription of Handwritten Text Images5.1 Introduction; 5.2 Formal Framework; 5.3 Adapting the Language Model; 5.4 Searching; 5.5 Experimental Framework; 5.5.1 Corpora; 5.5.2 Assessment Measures; 5.6 Results; 5.7 Conclusions; 6. A Web-based Demonstrator of Interactive Multimodal Transcription; 6.1 Introduction; 6.2 User Interaction Protocol; 6.3 System Description; 6.3.1 Application Programming Interface; 6.3.2 MM-CATTI Server; 6.3.3 Web Interface; 6.3.4 Electronic Pen or Touchscreen Interaction; 6.3.5 Keyboard and Mouse Interaction; 6.4 Evaluation 6.4.1 Assessment Measures6.4.2 Corpus; 6.4.3 Participants; 6.4.4 Apparatus; 6.4.5 Procedure; 6.4.6 Design; 6.5 Results and Discussion; 6.5.1 Quantitative Analysis; 6.5.1.1 Analysis of Time; 6.5.1.2 Analysis of rWER; 6.5.1.3 Analysis of WSR; 6.5.2 Qualitative Analysis; 6.5.3 Correlation Analysis; 6.5.3.1 Correlation between trials; 6.5.3.2 Correlation between metrics; 6.5.4 Limitations of the Study; 6.6 Conclusions; 7. Conclusions and Outlook; 7.1 Conclusions; 7.2 Outlook; Acknowledgements; Appendix A Symbols and Acronyms; A.1 Symbols; A.2 Acronyms; Bibliography; Index |
| Record Nr. | UNINA-9910465481503321 |
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Romer Verónica
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| Singapore ; ; Hackensack, NJ, : World Scientific Pub, c2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Multimodal Interactive handwritten text transcription [[electronic resource] /] / Verónica Romero, Alejandra Héctor Toselli, Enrique Vidal
| Multimodal Interactive handwritten text transcription [[electronic resource] /] / Verónica Romero, Alejandra Héctor Toselli, Enrique Vidal |
| Autore | Romer Verónica |
| Pubbl/distr/stampa | Singapore ; ; Hackensack, NJ, : World Scientific Pub, c2012 |
| Descrizione fisica | 1 online resource (180 p.) |
| Disciplina | 006.425 |
| Altri autori (Persone) |
ToselliAlejandro Héctor
VidalEnrique |
| Collana | Series in machine perception and artificial intelligence |
| Soggetto topico |
Writing - Data processing
Multimodal user interfaces (Computer systems) Human-computer interaction |
| ISBN |
1-283-59373-4
9786613906182 981-4390-34-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents; Preface; 1. Preliminaries; 1.1 Introduction; 1.2 State of the Art; 1.2.1 Optical Character Recognition; 1.2.2 Handwritten Text Recognition; 1.3 Formal Background; 1.3.1 Hidden Markov Models; Continuous HMM; Basic algorithms for HMMs; The Decoding Problem and the Viterbi Algorithm; The Learning Problem and the Baum-Welch Algorithm; 1.3.2 Language models: N-grams; n-grams modelled by a stochastic finite state automaton; 1.3.3 Interactive Pattern Recognition; 1.3.4 Word-graphs; 1.4 Assessing Computer Assisted Transcription of Handwritten Text Images; 2. Corpora; 2.1 Introduction
2.2 CS2.3 ODEC; 2.4 IAMDB; 2.5 UNIPEN; 3. Handwritten Text Recognition; 3.1 Introduction; 3.2 Off-line Handwritten Text Recognition; 3.2.1 Preprocessing; 3.2.2 Feature Extraction; 3.2.3 Recognition; 3.2.4 Experimental Framework; 3.2.5 Meta-parameter Adjustment Experiments; 3.2.6 Discussion of Results; 3.3 On-line Handwritten Text Recognition; 3.3.1 Preprocessing; 3.3.2 Feature Extraction; 3.3.3 Recognition; 3.3.4 Experimental Framework; 3.3.5 Results; 3.4 Summary and Conclusions; 4. Computer Assisted Transcription of Handwritten Text Images; 4.1 Introduction; 4.2 Formal Framework 4.3 Adapting the Language Model4.4 Searching; 4.4.1 Direct Viterbi-based Approach; 4.4.2 Word-graph based Approach; 4.4.2.1 Error-correction parsing; 4.5 Increasing Interaction Ergonomy; 4.5.1 Language Modelling and Search; 4.6 Interacting at the Character Level; 4.6.1 Language Modelling and Search; 4.7 Experimental Framework; 4.7.1 Assessment Measures; 4.7.2 Parameters and Meta-Parameters; 4.8 Results; Direct Viterbi-based approach; Word-graph based approach; Using Pointer Actions in the CATTI interaction process (PA- CATTI); CATTI at the character level; 4.9 Conclusions and Future Work 5. Multimodal Computer Assisted Transcription of Handwritten Text Images5.1 Introduction; 5.2 Formal Framework; 5.3 Adapting the Language Model; 5.4 Searching; 5.5 Experimental Framework; 5.5.1 Corpora; 5.5.2 Assessment Measures; 5.6 Results; 5.7 Conclusions; 6. A Web-based Demonstrator of Interactive Multimodal Transcription; 6.1 Introduction; 6.2 User Interaction Protocol; 6.3 System Description; 6.3.1 Application Programming Interface; 6.3.2 MM-CATTI Server; 6.3.3 Web Interface; 6.3.4 Electronic Pen or Touchscreen Interaction; 6.3.5 Keyboard and Mouse Interaction; 6.4 Evaluation 6.4.1 Assessment Measures6.4.2 Corpus; 6.4.3 Participants; 6.4.4 Apparatus; 6.4.5 Procedure; 6.4.6 Design; 6.5 Results and Discussion; 6.5.1 Quantitative Analysis; 6.5.1.1 Analysis of Time; 6.5.1.2 Analysis of rWER; 6.5.1.3 Analysis of WSR; 6.5.2 Qualitative Analysis; 6.5.3 Correlation Analysis; 6.5.3.1 Correlation between trials; 6.5.3.2 Correlation between metrics; 6.5.4 Limitations of the Study; 6.6 Conclusions; 7. Conclusions and Outlook; 7.1 Conclusions; 7.2 Outlook; Acknowledgements; Appendix A Symbols and Acronyms; A.1 Symbols; A.2 Acronyms; Bibliography; Index |
| Record Nr. | UNINA-9910792084103321 |
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Romer Verónica
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| Singapore ; ; Hackensack, NJ, : World Scientific Pub, c2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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