Titlebook: Document Analysis and Recognition - ICDAR 2023; 17th International C Gernot A. Fink,Rajiv Jain,Richard Zanibbi Conference proceedings 2023

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Accelerating Transformer-Based Scene Text Detection and?Recognition via?Token Pruning image features, which significantly reduces the number of tokens at the input of the transformer. Experimental results show how our network is able to obtain competitive results on multiple public benchmarks while running at significantly higher speeds.

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Text Enhancement: Scene Text Recognition in?Hazy Weatherent module and a recognition module. Extensive experiments on synthetic and real-world benchmarks demonstrate that our approach significantly improves the performance of popular text recognizers in adverse weather conditions.

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https://doi.org/10.1007/978-3-031-41731-3Document Analysis Systems; artificial intelligence; Document Layout and Parsing; Document Information E

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/e/image/282308.jpg

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https://doi.org/10.1007/978-3-030-30211-5tion of domain specific layout structures, and is further exacerbated by real-world image degradations such as perspective distortions. We propose a lightweight, scalable and generalizable approach to identify text reading order with a multi-modal, multi-task graph convolutional network (GCN) runnin

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Advances in Global Change Researchattention due to its essential role in various applications like understanding receipts, goods, and traffic signs. However, as existing benchmark datasets for VIE mainly consist of document images without the adequate diversity of layout structures, background disturbs, and entity categories, they c