001476149 000__ 05659cam\\22006857i\4500
001476149 001__ 1476149
001476149 003__ OCoLC
001476149 005__ 20231003174635.0
001476149 006__ m\\\\\o\\d\\\\\\\\
001476149 007__ cr\un\nnnunnun
001476149 008__ 230823s2023\\\\sz\a\\\\o\\\\\101\0\eng\d
001476149 019__ $$a1394118409
001476149 020__ $$a9783031415012$$q(electronic bk.)
001476149 020__ $$a3031415019$$q(electronic bk.)
001476149 020__ $$z9783031415005$$q(print)
001476149 0247_ $$a10.1007/978-3-031-41501-2$$2doi
001476149 035__ $$aSP(OCoLC)1395000801
001476149 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dOCLCQ$$dOCLCO
001476149 049__ $$aISEA
001476149 050_4 $$aTA1630$$b.I58 2023eb
001476149 08204 $$a006.4/2$$223/eng/20230823
001476149 1112_ $$aInternational Conference on Document Analysis and Recognition$$n(17th :$$d2023 :$$cSan José, Calif. ; Online)
001476149 24510 $$aDocument analysis and recognition -- ICDAR 2023 Workshops :$$bSan José, CA, USA, August 24-26, 2023, Proceedings.$$nPart II /$$cMickael Coustaty, Alicia Fornés, editors.
001476149 2463_ $$aICDAR 2023
001476149 264_1 $$aCham, Switzerland :$$bSpringer,$$c2023.
001476149 300__ $$a1 online resource (xxiii, 321 pages) :$$billustrations (some color).
001476149 336__ $$atext$$btxt$$2rdacontent
001476149 337__ $$acomputer$$bc$$2rdamedia
001476149 338__ $$aonline resource$$bcr$$2rdacarrier
001476149 4901_ $$aLecture notes in computer science,$$x1611-3349 ;$$v14194
001476149 500__ $$aIncludes author index.
001476149 5050_ $$aTypefaces and Ligatures in Printed Arabic Text: A Deep Learning-Based OCR Perspective -- Leveraging Knowledge Graph Embeddings to Enhance Contextual Representations for Relation Extraction -- Extracting Key-Value Pairs in Business Documents -- Long-Range Transformer Architectures for Document Understanding.-Pre-training transformers for Corporate Documents Understanding -- Transformer-Based Neural Machine Translation for Post-OCR Error Correction in Cursive Text -- Arxiv Tables: Document Understanding Challenge Linking Texts and Tables -- Subgraph-Induced Extraction Technique for Information (SETI) from Administrative Documents -- Document Layout Annotation: Database and Benchmark in the Domain of Public Affairs -- A Clustering Approach Combining Lines and Text Detection for Table Extraction -- Absformer: Transformer-Based Model for Unsupervised Multi-Document Abstractive Summarization -- A Comparison of Demographic Attributes Detection from Handwriting Based on Traditional and Deep Learning Methods -- A New Optimization Approach to Improve an Ensemble Learning Model: Application to Persian/Arabic Handwritten Character Recognition -- BN-DRISHTI: Bangla Document Recognition Through Instance-level Segmentation of Handwritten Text Images -- Text Line Detection and Recognition of Greek Polytonic Documents -- A Comprehensive Handwritten Paragraph Text Recognition System: LexiconNet -- Local Style Awareness of Font Images -- Fourier Feature-Based CBAM and Vision Transformer for Text Detection in Drone Images -- Document Binarization with Quaternionic Double Discriminator Generative Adversarial Network -- Crosslingual Handwritten Text Generation Using GANs -- Knowledge Integration inside Multitask Network for Analysis of Unseen ID Types.
001476149 506__ $$aAccess limited to authorized users.
001476149 520__ $$aThis two-volume set LNCS 14193-14194 constitutes the proceedings of International Workshops co-located with the 17th International Conference on Document Analysis and Recognition, ICDAR 2023, held in San José, CA, USA, during August 21-26, 2023. The total of 43 regular papers presented in this book were carefully selected from 60 submissions. Part I contains 22 regular papers that stem from the following workshops: ICDAR 2023 Workshop on Computational Paleography (IWCP); ICDAR 2023 Workshop on Camera-Based Document Analysis and Recognition (CBDAR); ICDAR 2023 International Workshop on Graphics Recognition (GREC); ICDAR 2023 Workshop on Automatically Domain-Adapted and Personalized Document Analysis (ADAPDA); Part II contains 21 regular papers that stem from the following workshops: ICDAR 2023 Workshop on Machine Vision and NLP for Document Analysis (VINALDO); ICDAR 2023 International Workshop on Machine Learning (WML). .
001476149 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed August 23, 2023).
001476149 650_0 $$aOptical data processing$$vCongresses.
001476149 650_0 $$aDocument imaging systems$$vCongresses.
001476149 650_0 $$aText processing (Computer science)$$vCongresses.
001476149 650_6 $$aTraitement optique de l'information$$vCongrès.
001476149 650_6 $$aGestion électronique de documents$$vCongrès.
001476149 650_6 $$aTraitement de texte$$vCongrès.
001476149 655_0 $$aElectronic books.
001476149 7001_ $$aCoustaty, Mickael,$$eeditor.
001476149 7001_ $$aFornés, Alicia,$$eeditor.
001476149 77608 $$iPrint version:$$aCoustaty, Mickael$$tDocument Analysis and Recognition - ICDAR 2023 Workshops$$dCham : Springer,c2023$$z9783031415005
001476149 830_0 $$aLecture notes in computer science ;$$v14194.$$x1611-3349
001476149 852__ $$bebk
001476149 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-41501-2$$zOnline Access$$91397441.1
001476149 909CO $$ooai:library.usi.edu:1476149$$pGLOBAL_SET
001476149 980__ $$aBIB
001476149 980__ $$aEBOOK
001476149 982__ $$aEbook
001476149 983__ $$aOnline
001476149 994__ $$a92$$bISE