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Document Analysis and Recognition ICDAR 2023 Workshops: San José, CA, USA, August 2426, 2023, Proceedings, Part II 1st ed. 2023 [Pehme köide]

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  • Formaat: Paperback / softback, 321 pages, kõrgus x laius: 235x155 mm, kaal: 534 g, 95 Illustrations, color; 101 Illustrations, black and white; XXIII, 321 p. 196 illus., 95 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 14194
  • Ilmumisaeg: 15-Aug-2023
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031415000
  • ISBN-13: 9783031415005
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Document Analysis and Recognition ICDAR 2023 Workshops: San José, CA, USA, August 2426, 2023, Proceedings, Part II 1st ed. 2023Suurem pilt
  • Formaat: Paperback / softback, 321 pages, kõrgus x laius: 235x155 mm, kaal: 534 g, 95 Illustrations, color; 101 Illustrations, black and white; XXIII, 321 p. 196 illus., 95 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 14194
  • Ilmumisaeg: 15-Aug-2023
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031415000
  • ISBN-13: 9783031415005
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783031415005.html
Märksõnad:
This two-volume set LNCS 14193-14194 constitutes the proceedings of International Workshops co-located with the 17th International Conference on Document Analysis and Recognition, ICDAR 2023, held in San José, CA, USA, during August 2126, 2023.





The total of 43 regular papers presented in this book were carefully selected from 60 submissions. 





Part I contains 22 regular papers that stem from the following workshops:





ICDAR 2023 Workshop on Computational Paleography (IWCP);





ICDAR 2023 Workshop on Camera-Based Document Analysis and Recognition (CBDAR);





ICDAR 2023 International Workshop on Graphics Recognition (GREC);





ICDAR 2023 Workshop on Automatically Domain-Adapted and Personalized Document Analysis (ADAPDA);





Part II contains 21 regular papers that stem from the following workshops:





ICDAR 2023 Workshop on Machine Vision and NLP for Document Analysis (VINALDO);





ICDAR 2023 International Workshop on MachineLearning (WML).





 
Typefaces and Ligatures in Printed Arabic Text: A Deep Learning-Based
OCR Perspective.- Leveraging Knowledge Graph Embeddings to Enhance Contextual
Representations for Relation Extraction.- Extracting Key-Value Pairs in
Business Documents.- Long-Range Transformer Architectures for Document
Understanding.-Pre-training transformers  for Corporate Documents
Understanding.- Transformer-Based Neural Machine Translation for Post-OCR
Error Correction in Cursive Text.- Arxiv Tables: Document Understanding
Challenge Linking Texts and Tables.- Subgraph-Induced Extraction Technique
for Information (SETI) from Administrative Documents.- Document Layout
Annotation: Database and Benchmark in the Domain of Public Affairs.- A
Clustering Approach Combining Lines and Text Detection for Table Extraction.-
Absformer: Transformer-Based Model for Unsupervised Multi-Document
Abstractive Summarization.- A Comparison of Demographic Attributes Detection
from Handwriting Based on Traditional and Deep Learning Methods.- A New
Optimization Approach to Improve an Ensemble Learning Model: Application to
Persian/Arabic Handwritten Character Recognition.- BN-DRISHTI: Bangla
Document Recognition Through Instance-level Segmentation of Handwritten Text
Images.- Text Line Detection and Recognition of Greek Polytonic Documents.- A
Comprehensive Handwritten Paragraph Text Recognition System: LexiconNet.-
Local Style Awareness of Font Images.- Fourier Feature-Based CBAM and Vision
Transformer for Text Detection in Drone Images.- Document Binarization with
Quaternionic Double Discriminator Generative Adversarial Network.-
Crosslingual Handwritten Text Generation Using GANs.- Knowledge Integration
inside Multitask Network for Analysis of Unseen ID Types.