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[null,"1.","Record Nr.",null,"UNINA9910741163603321"]
[null,null,"Autore",null,"Coustaty Mickael"]
[null,null,"Titolo",null,"Document Analysis and Recognition – ICDAR 2023 Workshops : San José, CA, USA, August 24–26, 2023, Proceedings, Part I / / edited by Mickael Coustaty, Alicia Fornés"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023"]
[null,null,"ISBN",null,"9783031414985"]
[{"$nulls":4},"3031414985"]
[null,null,"Edizione",null,"[1st ed. 2023.]"]
[null,null,"Descrizione fisica",null,"1 online resource (344 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 1611-3349 ; ; 14193"]
[null,null,"Disciplina",null,"006"]
[null,null,"Soggetti",null,"Image processing - Digital techniques"]
[{"$nulls":4},"Computer vision"]
[{"$nulls":4},"Machine learning"]
[{"$nulls":4},"Database management"]
[{"$nulls":4},"Natural language processing (Computer science)"]
[{"$nulls":4},"Social sciences - Data processing"]
[{"$nulls":4},"Information storage and retrieval systems"]
[{"$nulls":4},"Computer Imaging, Vision, Pattern Recognition and Graphics"]
[{"$nulls":4},"Machine Learning"]
[{"$nulls":4},"Database Management"]
[{"$nulls":4},"Natural Language Processing (NLP)"]
[{"$nulls":4},"Computer Application in Social and Behavioral Sciences"]
[{"$nulls":4},"Information Storage and Retrieval"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Beyond Human Forgeries: An Investigation into Detecting Diffusion-Generated Handwriting -- Leveraging Large Language Models for Topic Classification in the Domain of Public Affairs -- The Adaptability of a Transformer-based OCR Model for Historical Documents -- Using GANs for Domain Adaptive High Resolution Synthetic Document Generation -- A Survey and Approach to Chart Classification -- Segmentation-Free Alignment of Arbitrary Symbol Transcripts to Images -- Optical Music Recognition: Recent Advances, Current Challenges, and Future Directions -- Reconstruction of Power Lines from Point Clouds -- KangaiSet : A Dataset for Visual Emotion Recognition on Manga -- MuraNet: Multi-task Floor Plan Recognition "]
[{"$nulls":4},"with Relation Attention -- Automatic Detection of Comic Characters: An Analysis of Model Robustness Across Domains -- FPNet: Deep Attention Network for Automated Floor Plan Analysis -- Detection of buried complex text. Case of Onomatopoeia in comics books -- Text Extraction for Handwritten CircuitDiagram Images -- Can Pre-trained Language Models help in Understanding Handwritten Symbols? -- On Text Localization in End-to-End OCR-Free Document Understanding Transformer without Text Localization Supervision -- IndicSTR12: A Dataset for Indic Scene Text Recognition -- Reconstruction of Broken Writing Strokes in Greek Papyri -- Collaborative Annotation and Computational Analysis of Hieratic -- Efficient Annotation of Medieval Charters -- Greek Literary Papyri Dating Benchmark -- Stylistic Similarities in Greek Papyri based on Letter Shapes: A Deep Learning approach."]
[null,null,null,"Sommario/riassunto","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 21–26, 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). ."]