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

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https://doi.org/10.1007/978-3-319-89734-9fferent learning-free document analysis tasks. While machine learning is rather unexplored for graph representations, geometric deep learning offers a novel framework that allows for convolutional neural networks similar to the image domain. In this work, we show that the concept of attribute predic

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https://doi.org/10.1007/978-981-10-8609-0 easily deployed in production and extended for further investigation. However, various factors like loosely organized codebases and sophisticated model configurations complicate the easy reuse of important innovations by a wide audience. Though there have been on-going efforts to improve reusabilit

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https://doi.org/10.1007/978-94-007-2315-3ion is a common process in business workflows, there is a dire need of analyzing the potential of compressed models for the task of document image classification. Surprisingly, no such analysis has been done in the past. Furthermore, once a compressed model is obtained using a particular compression

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