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Frontiers in Handwriting Recognition: 18th International Conference, ICFHR 2022, Hyderabad, India, December 4–7, 2022, Proceedings de Utkarsh Porwal

Frontiers in Handwriting Recognition: 18th International Conference, ICFHR 2022, Hyderabad, India, December 4–7, 2022, Proceedings: Lecture Notes in Computer Science, cartea 13639

Editat de Utkarsh Porwal, Alicia Fornés, Faisal Shafait
en Limba Engleză Paperback – 20 noi 2022
This book constitutes the refereed proceedings of the 18th International Conference on Frontiers in Handwriting Recognition, ICFHR 2022, which took place in Hyderabad, India, during December 4-7, 2022.

The 36 full papers and 1 short paper presented in this volume were carefully reviewed and selected from 61 submissions. The contributions were organized in topical sections as follows: Historical Document Processing; Signature Verification and Writer Identification; Symbol and Graphics Recognition; Handwriting Recognition and Understanding; Handwriting Datasets and Synthetic Handwriting Generation; Document Analysis and Processing.


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Specificații

ISBN-13: 9783031216473
ISBN-10: 3031216474
Pagini: 564
Ilustrații: XIV, 564 p. 210 illus., 159 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.8 kg
Ediția:1st ed. 2022
Editura: Springer International Publishing
Colecția Springer
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

​Historical Document Processing.- A Few Shot Multi-Representation Approach for N-gram Spotting in Historical Manuscripts.- Text Edges Guided Network for Historical Document Super Resolution.- CurT: End-to-End Text Line Detection in Historical Documents with Transformers.- Date Recognition in Historical Parish Records.- Improving Isolated Glyph Classification Task for Palm leaf Manuscripts.- Signature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.