Titlebook: Document Analysis and Recognition – ICDAR 2021; 16th International C Josep Lladós,Daniel Lopresti,Seiichi Uchida Conference proceedings 202

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Competition and Collaboration in Document Analysis and Recognitioncified tasks. We comment on the?~?100 citations garnered by these contests over the intervening 3.5?years. Finally, in what we consider a logical sequel, we speculate on the possibility of an alternative model of small-scale, short-range communal research based on collaboration that seems to offer benefits competitions cannot capture.

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Online Spatio-temporal 3D Convolutional Neural Network for Early Recognition of Handwritten Gesturesin time if more information is needed. Moreover our system is end-to-end trainable, OLT-C3D and the temporal reject system are jointly trained to optimize the earliness of the decision. Our approach achieves superior performances on two complementary and freely available datasets: ILGDB and MTGSetB.

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0302-9743 istorical document analysis, document analysis systems, handwriting recognition, scene text detection and recognition, document image processing, natural language processing (NLP) for document understanding, and graphics, diagram and math recognition..978-3-030-86548-1978-3-030-86549-8Series ISSN 0302-9743 Series E-ISSN 1611-3349

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0302-9743 Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021.?The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports..The papers are organized into the following topical sections: h

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978-3-030-86548-1Springer Nature Switzerland AG 2021

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Document access — Networks and Convertersns contain densely laid out, highly irregular and overlapping multi-class region instances with large range in aspect ratio. Fully automatic boundary estimation approaches tend to be data intensive, cannot handle variable-sized images and produce sub-optimal results for aforementioned images. To add