Titlebook: Document Analysis Systems; 14th IAPR Internatio Xiang Bai,Dimosthenis Karatzas,Daniel Lopresti Conference proceedings 2020 Springer Nature

淺灘

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Shinichi Ichimura,Tsuneaki Satoical character recognition (OCR) performance prior to any actual recognition, but also provides immediate feedback on whether the documents meet the quality requirements for other high level document processing and analysis tasks. In this work, we present a deep neural network (DNN) to accomplish th

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Arie Kuyvenhoven,Olga Memedovic,Nico Windts work we focus on decorated background removal and the extraction of textual components from French university diploma. As far as we know, this is the very first attempt to resolve this kind of problem on French university diploma images. Hence, we make our dataset public for further research, rela

最初

Transition in Central and Eastern Europeon is a key step in table understanding. Nowadays, the most successful methods for table detection in document images employ deep learning algorithms; and, particularly, a technique known as .. In this context, such a technique exports the knowledge acquired to detect objects in natural images to de

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Arie Kuyvenhoven,Olga Memedovic,Nico Windtmanually annotating the bounding boxes of graphical or page objects in publicly available annual reports. This dataset contains a total of 13. annotated page images with objects in five different popular categories—table, figure, natural image, logo, and signature. It is the largest manually annotat

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致命

Maximum Entropy Regularization and Chinese Text Recognitionlasses, which causes a serious overfitting problem. We propose to apply Maximum Entropy Regularization to regularize the training process, which is to simply add a negative entropy term to the canonical cross-entropy loss without any additional parameters and modification of a model. We theoreticall